Journal articles: 'Intelligent Voice Assistant'

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Wolters, Maria Klara, Fiona Kelly, and Jonathan Kilgour. "Designing a spoken dialogue interface to an intelligent cognitive assistant for people with dementia." Health Informatics Journal 22, no. 4 (July 26, 2016): 854–66. http://dx.doi.org/10.1177/1460458215593329.

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Intelligent cognitive assistants support people who need help performing everyday tasks by detecting when problems occur and providing tailored and context-sensitive assistance. Spoken dialogue interfaces allow users to interact with intelligent cognitive assistants while focusing on the task at hand. In order to establish requirements for voice interfaces to intelligent cognitive assistants, we conducted three focus groups with people with dementia, carers, and older people without a diagnosis of dementia. Analysis of the focus group data showed that voice and interaction style should be chosen based on the preferences of the user, not those of the carer. For people with dementia, the intelligent cognitive assistant should act like a patient, encouraging guide, while for older people without dementia, assistance should be to the point and not patronising. The intelligent cognitive assistant should be able to adapt to cognitive decline.

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Tanribilir, Rusen N. "Analysing antecedence of an intelligent voice assistant use intention and behaviour." F1000Research 10 (June 25, 2021): 496. http://dx.doi.org/10.12688/f1000research.52637.1.

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Background: As the use of intelligent voice assistant applications becomes more prevalent, a growing body of studies are examining individuals' interactions with intelligent voice assistants. However, very limited research has focused on comparing the antecedents of both use and non-use behaviour of individuals, based on the technology acceptance models. To fill this gap, the present study investigated antecedents of intelligent voice assistance use and use intention in a cross-sectional setting. Additionally, to go one step beyond the existing literature on technology acceptance models and theories, a new construct termed perceived needs, as well as the moderating role of perceived privacy concerns and perceived awareness, are introduced. Method: A quantitative, cross-sectional research design was utilised using a nonprobability sampling strategy through the online networking platforms. Total of 277 (n = 155 users vs n = 122 non-users) international adults age between 20-74 years (79.6% female, 20.4 % male) contributed to the study. Ordinary least squares (OLS) linear regression and Bivariate logistic regression analyses for non-users and users were conducted, respectively. Results: Both analyses revealed that peer influence and perceived needs related to the intention to use intelligent voice assistants for non-users, which applied to the current intelligent voice assistance users where privacy concerns were considered. Surprisingly, the key determinants of technology acceptance and use theories, such as perceived ease of use and perceived usefulness, did not hold for intelligent voice assistance usage. Conclusion: The current research contributed to the field by validating new constructs of perceived needs and the moderation role of perceived privacy concerns. However, in order to build on an existing body of knowledge, future studies should further examine the moderation role of perceived privacy concerns, perceived ease of use, and perceived usefulness in the same domain.

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Raut, Darpan, Sumedh Salvi, Shrikrishna Salvi, and S. R. Rangari. "Intelligent Voice Assistant using Android Platform." International Journal of Computer Trends and Technology 34, no. 3 (April 25, 2016): 160–63. http://dx.doi.org/10.14445/22312803/ijctt-v34p129.

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Akash Roshan and Meenu Garg, Dr Neha Agrawal. "Intelligent Voice Assisstant for Desktop using NLP and AI." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 15, 2020): 328–31. http://dx.doi.org/10.46501/ijmtst061261.

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In the coming future, virtual assistants will automate nearly all of the manual and time-consuming tasks. This project is an implementation of an intelligent voice assistant for Windows which includes the functionality of security through facial recognition. Until this day, there has not been any good alternative for Windows, so this project aims to implement a voice assistant for the Windows platform while describing the difficulties and challenges that lies in this task.

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Bajaj, Divij, and Dhanya Pramod. "Conversational System, Intelligent Virtual Assistant (IVA) Named DIVA Using Raspberry Pi." International Journal of Security and Privacy in Pervasive Computing 12, no. 4 (October 2020): 38–52. http://dx.doi.org/10.4018/ijsppc.2020100104.

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Humans are living in an era where they are interacting with machines day in and day out. In this new era of the 21st century, a virtual assistant (IVA) is a boon for everyone. It has opened the way for a new world where devices can interact their own. The human voice is integrated with every device making it intelligent. These IVAs can also be used to integrate it with business intelligence software such as Tableau and PowerBI to give dashboards the power of voice and text insights using NLG (natural language generation). This new technology attracted almost the entire world like smart phones, laptops, computers, smart meeting rooms, car InfoTech system, TV, etc. in many ways. Some of the popular voice assistants are like Mibot, Siri, Google Assistant, Cortana, Bixby, and Amazon Alexa. Voice recognition, contextual understanding, and human interaction are some of the issues that are continuously improving in these IVAs and shifting this paradigm towards AI research. This research aims at processing human natural voice and gives a meaningful response to the user. The questions that it is not able to answer are stored in a database for further investigation.

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Berdasco, López, Diaz, Quesada, and Guerrero. "User Experience Comparison of Intelligent Personal Assistants: Alexa, Google Assistant, Siri and Cortana." Proceedings 31, no. 1 (November 20, 2019): 51. http://dx.doi.org/10.3390/proceedings2019031051.

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Natural user interfaces are becoming popular. One of the most common natural user interfaces nowadays are voice activated interfaces, particularly smart personal assistants such as Google Assistant, Alexa, Cortana, and Siri. This paper presents the results of an evaluation of these four smart personal assistants in two dimensions: the correctness of their answers and how natural the responses feel to users. Ninety-two participants conducted the evaluation. Results show that Alexa and Google Assistant are significantly better than Siri and Cortana. However, there is no statistically significant difference between Alexa and Google Assistant.

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Li, Juan, Bikesh Maharjan, Bo Xie, and Cui Tao. "A Personalized Voice-Based Diet Assistant for Caregivers of Alzheimer Disease and Related Dementias: System Development and Validation." Journal of Medical Internet Research 22, no. 9 (September 21, 2020): e19897. http://dx.doi.org/10.2196/19897.

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Background The world’s aging population is increasing, with an expected increase in the prevalence of Alzheimer disease and related dementias (ADRD). Proper nutrition and good eating behavior show promise for preventing and slowing the progression of ADRD and consequently improving patients with ADRD’s health status and quality of life. Most ADRD care is provided by informal caregivers, so assisting caregivers to manage patients with ADRD’s diet is important. Objective This study aims to design, develop, and test an artificial intelligence–powered voice assistant to help informal caregivers manage the daily diet of patients with ADRD and learn food and nutrition-related knowledge. Methods The voice assistant is being implemented in several steps: construction of a comprehensive knowledge base with ontologies that define ADRD diet care and user profiles, and is extended with external knowledge graphs; management of conversation between users and the voice assistant; personalized ADRD diet services provided through a semantics-based knowledge graph search and reasoning engine; and system evaluation in use cases with additional qualitative evaluations. Results A prototype voice assistant was evaluated in the lab using various use cases. Preliminary qualitative test results demonstrate reasonable rates of dialogue success and recommendation correctness. Conclusions The voice assistant provides a natural, interactive interface for users, and it does not require the user to have a technical background, which may facilitate senior caregivers’ use in their daily care tasks. This study suggests the feasibility of using the intelligent voice assistant to help caregivers manage patients with ADRD’s diet.

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Yamsaengsung, Siam, and Borworn Papasratorn. "Conceptual Model for an Intelligent Persuasive Driver Assistant." KnE Social Sciences 3, no. 1 (January 15, 2018): 157. http://dx.doi.org/10.18502/kss.v3i1.1404.

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Traffic congestion is a serious issue for large cities. This is especially critical for cities that has insufficient mass transit system like Bangkok. Although transportation infrastructure projects and rail mass transit lines are being implemented, these efforts require major financial investment and take a long time to complete. This work proposes to help reduce traffic problems through influencing a change in driver behavior. In this initial stage, a model for an intelligent persuasive driver assistant is conceptualized as a voice-interactive smart assistant on a smartphone. The system uses information about the driver, his physical state, vehicle performance information, and geolocation information to form persuasive strategies to influence driver behavior and to adapt user interfaces and interactions to reduce driver distraction. Integrating these components together is expected to provide improved assistance in driving tasks and affect driving behavior changes. Keywords: intelligent driver assistant, navigation, smart assistant, persuasive technology

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Cuadra, Andrea, Shuran Li, Hansol Lee, Jason Cho, and Wendy Ju. "My Bad! Repairing Intelligent Voice Assistant Errors Improves Interaction." Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (April 13, 2021): 1–24. http://dx.doi.org/10.1145/3449101.

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Srivastava, Nayan. "A.I.- Smart Assistant." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 25, 2021): 2384–87. http://dx.doi.org/10.22214/ijraset.2021.35553.

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Artificial Intelligent is widely used when it comes to everyday life. Computer science explains AI research as a study of brilliant agents. Every In almost any direction one turns to today, some computer-based information processing technology intervention, whether by a person knowingly or not. Artificial Intelligence has already changed our way of life. A device that we can see in its nature and take action increases its chances of success goals. Input to the recommendations algorithm can be a file for user database and items, and careless removal will be recommendations. User to install in the system by voice or text. This paper presents a new approach to it. All over the world, many people use the assistant. This paper introduces visual applications an assistant that helps to give humanity a chance at various domains. This paper also describes the annoyance of using visual assistant technology.

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Subhash S., Siddesh S., Prajwal N. Srivatsa, Ullas A., and Santhosh B. "Developing a Graphical User Interface for an Artificial Intelligence-Based Voice Assistant." International Journal of Organizational and Collective Intelligence 11, no. 3 (July 2021): 49–67. http://dx.doi.org/10.4018/ijoci.2021070104.

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Artificial intelligence machineries have been extensively active in human life in recent times. Self-governing devices are enhancing their way of interacting with both human and devices. Contemporary vision in this topic can pave the way for a new process of human-machine interaction in which users will get to know how people can understand human language, adapting and communicating through it. One such tool is voice assistant, which can be incorporated into many other brilliant devices. In this article, the voice assistant will receive the audio from the microphone and then convert that into text, later with the help of ‘pyttsx3', and then the text response will be converted into an audio file; then the audio file will be played. The audio is processed using the voice user interface (VUI). This article develops a functional intelligent personal assistant (IPA) and integrates it with a graphical user interface that can perform mental tasks such as ON/OFF of smart applications based on the user commands.

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Shashwat Shukla., Srishti Sinha.,, Sohan Singh &. Anupam Lakhanpal. "Jarvis: Desktop Assistant." International Journal for Modern Trends in Science and Technology 7, no. 05 (May 27, 2021): 178–83. http://dx.doi.org/10.46501/ijmtst0705030.

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“Jarvis” was main character of Tony’s Stark’s life assistant in Movies Iron Man. Unlike original comic in which Jarvis was Stark’s human butler, the movie version of Jarvis is an intelligent computer that converses with stark, monitors his household and help to build and program his superhero suit. In this Project Jarvis is Digital Life Assistant which uses mainly human communication means such Twitter, instant message and voice to create two way connections between human and his apartment, controlling lights and appliances, assist in cooking, notify him of breaking news, Facebook’s Notifications and many more. In our project we mainly use voice as communication means so the Jarvis is basically the Speech recognition application. The concept of speech technology really encompasses two technologies: Synthesizer and recognizer. A speech synthesizer takes as input and produces an audio stream as output. A speech recognizer on the other hand does opposite. It takes an audio stream as input and thus turns it into text transcription. The voice is a signal of infinite information. A direct analysisand synthesizing the complex voice signal is due to too much information contained in the signal. Therefore the digital signal processes such as Feature Extraction and Feature Matching are introduced to represent the voice signal. In this project we directly use speech engine which use Feature extraction technique as Mel scaled frequency cepstral. The mel- scaled frequency cepstral coefficients (MFCCs) derived from Fourier transform and filter bank analysis are perhaps the most widely used front- ends in state-of-the-art speech recognition systems. Our aim to create more and more functionalities which can help human to assist in their daily life and also reduces their efforts. In our test we check all this functionality is working properly. We test this on 2 speakers(1 Female and 1 Male) for accuracy purpose.

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Poongothai, M., K. Sundar, and B. Vinayak. "Implementation of IoT based Intelligent Voice Controlled Laboratory using Google Assistant." International Journal of Computer Applications 182, no. 16 (September 17, 2018): 6–10. http://dx.doi.org/10.5120/ijca2018917808.

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Barata, Marcos, Afan Galih Salman, Ikhtiar Faahakhododo, and Bayu Kanigoro. "Android based voice assistant for blind people." Library Hi Tech News 35, no. 6 (August 6, 2018): 9–11. http://dx.doi.org/10.1108/lhtn-11-2017-0083.

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Purpose The purpose of this study is to discuss the development of Android-based Intelligent Software Assistant application for visually challenged or blind people. The application is intended to help people with visual limitations or blind people to access Android-based devices so that they can use library resources by using android devices. Design/methodology/approach The necessary data are collected from journals, articles, books and questionnaires, and similar applications are analyzed. The application design method used is the Scrum method, which consists of Backlog, Sprint and Scrum Meeting. From the operational side of the application, the method used is speech-to-text and text-to-speech. Findings This application has been tried with some users who have total blindness and low vision, and all provided a good response to this application. From the performance side, the user gives a very satisfied response to this application. While the ease of using the application, the user also provides a satisfactory response to the ease of using this application. Research limitations/implications The application still has limitations in penetration to the user, and the application is only built using Android as its platform. In addition, the dependence on libraries from Google has caused difficulties in implementing this application with local dialect, which is only understood by the local community. Social implication This application has implications for the society, especially those with limitations in eyesight can be so much more productive and independent. This can reduce the social burden in society. Originality/value This application provides an easy access of an android device to blind people and people with low vision, as well as access to library resources with devices that have been installed with this application. This facility can improve the library accessibility to the blind and visually challenged community.

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Et. al., Vijay A. Kotkar,. "Interactive Robot for Automated Question and Answer System." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (April 11, 2021): 90–95. http://dx.doi.org/10.17762/turcomat.v12i1s.1566.

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This paper aims in developing an intelligent interactive robot with multi-functions which provides entertainment and companion. To obtain the information accurately, we have used speech recognition to perform the operations. For the Robot behavior, planning, interactions with the voice assistant and interactions with the user the various speech recognition results are applied. The robot has a simple design. In this study, we have used the microphone for speech recognition. In addition, we have used room automation, notice display using voice message of the intelligent interaction between human and robots.

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Kim, Min Jung. "The Study on Consideration of Voice User Interface Design - Focused on Intelligent Voice Assistant on Smartphones -." JOURNAL OF THE KOREAN SOCIETY DESIGN CULTURE 23, no. 3 (September 30, 2017): 57–66. http://dx.doi.org/10.18208/ksdc.2017.23.3.57.

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Ermolina, Alena, and Victor Tiberius. "Voice-Controlled Intelligent Personal Assistants in Health Care: International Delphi Study." Journal of Medical Internet Research 23, no. 4 (April 9, 2021): e25312. http://dx.doi.org/10.2196/25312.

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Background Voice-controlled intelligent personal assistants (VIPAs), such as Amazon Echo and Google Home, involve artificial intelligence–powered algorithms designed to simulate humans. Their hands-free interface and growing capabilities have a wide range of applications in health care, covering off-clinic education, health monitoring, and communication. However, conflicting factors, such as patient safety and privacy concerns, make it difficult to foresee the further development of VIPAs in health care. Objective This study aimed to develop a plausible scenario for the further development of VIPAs in health care to support decision making regarding the procurement of VIPAs in health care organizations. Methods We conducted a two-stage Delphi study with an internationally recruited panel consisting of voice assistant experts, medical professionals, and representatives of academia, governmental health authorities, and nonprofit health associations having expertise with voice technology. Twenty projections were formulated and evaluated by the panelists. Descriptive statistics were used to derive the desired scenario. Results The panelists expect VIPAs to be able to provide solid medical advice based on patients’ personal health information and to have human-like conversations. However, in the short term, voice assistants might neither provide frustration-free user experience nor outperform or replace humans in health care. With a high level of consensus, the experts agreed with the potential of VIPAs to support elderly people and be widely used as anamnesis, informational, self-therapy, and communication tools by patients and health care professionals. Although users’ and governments’ privacy concerns are not expected to decrease in the near future, the panelists believe that strict regulations capable of preventing VIPAs from providing medical help services will not be imposed. Conclusions According to the surveyed experts, VIPAs will show notable technological development and gain more user trust in the near future, resulting in widespread application in health care. However, voice assistants are expected to solely support health care professionals in their daily operations and will not be able to outperform or replace medical staff.

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Meza, Andrés, Gustavo López, Luis Quesada, and Luis A. Guerrero. "Architecture to Design Booking Appointment Applications for the Smart Personal Assistant Alexa." Proceedings 31, no. 1 (November 20, 2019): 17. http://dx.doi.org/10.3390/proceedings2019031017.

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The intelligent smart assistants are becoming more interactive and helpful for everyday tasks. The Amazon Echo has potential for advanced voice interactions and as a tool for conducting complex tasks. The potential of the Amazon Echo in the area of booking appointments is not being fully exploited by developers. A flexible architecture for developing appointment booking applications for the Amazon Echo was proposed. The architecture serves as guide for developers without experience working with Voice User Interfaces and saves development time by abstracting the complexity of voice interactions. A prototype skill was developed following the architecture principles and evaluated by a group of users. The skill successfully defines how an appointment booking skill should be.

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Al-Kaisi, Alisa Nazarovna, Alla Leonidovna Arkhangel’skaya, and Ol’ga Ivanovna Rudenko-Morgun. "INTELLIGENT VOICE ASSISTANT ALICE IN CLASSES OF RUSSIAN AS A FOREIGN LANGUAGE (LEVEL A1)." Philological Sciences. Issues of Theory and Practice, no. 2 (February 2019): 239–44. http://dx.doi.org/10.30853/filnauki.2019.2.52.

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Azaria, Amos, and Keren Nivasch. "SAIF: A Correction-Detection Deep-Learning Architecture for Personal Assistants." Sensors 20, no. 19 (September 29, 2020): 5577. http://dx.doi.org/10.3390/s20195577.

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Intelligent agents that can interact with users using natural language are becoming increasingly common. Sometimes an intelligent agent may not correctly understand a user command or may not perform it properly. In such cases, the user might try a second time by giving the agent another, slightly different command. Giving an agent the ability to detect such user corrections might help it fix its own mistakes and avoid making them in the future. In this work, we consider the problem of automatically detecting user corrections using deep learning. We develop a multimodal architecture called SAIF, which detects such user corrections, taking as inputs the user’s voice commands as well as their transcripts. Voice inputs allow SAIF to take advantage of sound cues, such as tone, speed, and word emphasis. In addition to sound cues, our model uses transcripts to determine whether a command is a correction to the previous command. Our model also obtains internal input from the agent, indicating whether the previous command was executed successfully or not. Finally, we release a unique dataset in which users interacted with an intelligent agent assistant, by giving it commands. This dataset includes labels on pairs of consecutive commands, which indicate whether the latter command is in fact a correction of the former command. We show that SAIF outperforms current state-of-the-art methods on this dataset.

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Hüsson, Daniel, Alexander Holland, and Rocío Arteaga Sánchez. "Intelligent Personal Assistant in Business-Context: Key-feature Evaluation for User Acceptance." Business Systems Research Journal 11, no. 3 (November 1, 2020): 147–66. http://dx.doi.org/10.2478/bsrj-2020-0032.

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AbstractBackgroundThe usage of intelligent personal assistants (IPA), such as Amazon Alexa or Google Assistant is increasing significantly, and voice-interaction is relevant for workflows in a business context.ObjectivesThis research aims to determine IPA characteristics to evaluate the usefulness of specific functions in a simulated production system of an Enterprise Resource Planning (ERP) software. A new function called explanation-mode is introduced to the scientific community and business world.Methods/ApproachAs part of a design science research, an artefact, i.e. an add-on for speech-interaction in business software, was developed and evaluated using a survey among ERP users and researchers.ResultsIn the area of IPA-features, the search-function and speech input for textual fields were recognised as most useful. The newly introduced feature, the explanation mode, was positively received too. There is no significant correlation between the usefulness of features and participant-characteristics, affinity to technology or previous experience with IPAs in a private context, which is in line with previous studies in the private environment leading to the conclusion that the task attraction is the most important element for usefulness.ConclusionsMost of the participants agreed that the speech-input is not able to fully substitute standard input devices, such as a keyboard or a mouse, so the IPA is recognised as an addition to traditional input methods. The usefulness is rated high especially for speech-input for long text fields, calling up masks and search-functions.

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Lee, Changsu, and Youngjoong Ko. "Spoken Language Understanding with a Novel Simultaneous Recognition Technique for Intelligent Personal Assistant Software." International Journal on Artificial Intelligence Tools 27, no. 03 (May 2018): 1850009. http://dx.doi.org/10.1142/s0218213018500094.

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Intelligent personal assistant software, such as Apple’s Siri and Samsung’s S-Voice, is being widely used these days. One of the core modules of this kind of software is the spoken language understanding (SLU) module used to predict the user’s intention for determining the system actions. The SLU module usually consists of several connected recognition components on a pipeline framework, whereas the proposed SLU module is developed by a novel technique that can simultaneously recognize four recognition components, namely named entity, speech-act, target, and operation using conditional random fields. In the experiments, the proposed simultaneous recognition technique achieved a relative improvement as high as approximately 2.2% and a faster speed of approximately 15% compared to a pipeline framework. A significance test showed that this improvement was statistically significant because the p-value was smaller than 0.01.

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Fagundes Pase, André, Gisele Noll, Mariana Gomes da Fontoura, and Letícia Dallegrave. "Who Controls the Voice? The Journalistic Use and the Informational Domain in Vocal Transactors." Brazilian Journalism Research 16, no. 3 (December 29, 2020): 576–603. http://dx.doi.org/10.25200/bjr.v16n3.2021.1316.

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This article aims to understand the transformations caused by new informational ecosystems in contemporary journalism. This analysis is performed based on news accessed through personal digital assistants embedded in smart speakers. As a methodological procedure, it adopts a multiple case study, defining the vocal transactors of Google (Nest Home/Google Assistant) and Amazon (Echo/Alexa) as its object. Therefore, this paper notes that the inclusion of algorithmic routines and the extension of news content to intelligent voice interfaces requires adaptation for the personalization of information, an ecosystem that is feedback by traditional vehicles, journalists, and people who interact with the artifacts.O presente artigo tem como objetivo compreender as transformações causadas por novos ecossistemas informacionais no jornalismo contemporâneo. Essa análise é realizada a partir de notícias acessadas através de assistentes pessoais digitais embarcados em alto-falantes inteligentes. Como procedimento metodológico, adota o estudo de caso múltiplo, definindo como objeto os transatores vocais da Google (Nest Home/Google Assistant) e da Amazon (Echo/Alexa). Observa, portanto, que a inclusão de rotinas algorítmicas e a extensão de conteúdo noticioso para interfaces de voz inteligentes demanda adaptação para a personalização das informações, ecossistema que é retroalimentado por veículos tradicionais, jornalistas e pessoas que interagem com os artefatos.Este artículo tiene como objetivo comprender las transformaciones causadas por los nuevos ecosistemas informativos en el periodismo contemporáneo. Este análisis se realiza en función de las noticias a las que se accede a través de asistentes digitales personales integrados en altavoces inteligentes. Como procedimiento metodológico, adopta un estudio de caso múltiple, definiendo los transactores vocales de Google (Nest Home/Google Assistant) y Amazon (Echo/Alexa) como su objeto. Señala, por lo tanto, que la inclusión de rutinas algorítmicas y la extensión del contenido de noticias a interfaces de voz inteligentes requiere adaptación para la personalización de la información, un ecosistema que es retroalimentado por vehículos tradicionales, periodistas y personas que interactúan con los artefactos.

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Lozano Murciego, Álvaro, Diego M. Jiménez-Bravo, Denis Pato Martínez, Adrián Valera Román, and Gabino Luis Lazo. "Voice Assistant and Route Optimization System for Logistics Companies in Depopulated Rural Areas." Sustainability 12, no. 13 (July 3, 2020): 5377. http://dx.doi.org/10.3390/su12135377.

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Nowadays, the depopulation of Europe’s rural areas and the ageing of the population in these areas has led to the disappearance of basic services such as supermarkets, fishmongers, household goods, etc. In response to this problem, there are European and local initiatives to mitigate these effects by investing in Internet and Communication Technologies (ICT) infrastructure and logistics services for these areas with the aim of moving services to these isolated areas. The problem with these services lies in the difficulty of the use of ICT by aged people due to digital divide and, at the same time, the high cost in human resources, time, vehicles, and fuel presented by delivery services in these areas. This work presents a system that combines the use of intelligent personal assistants (IPA) to facilitate access to information technology for aged people to place orders and the optimization of delivery routes in a rural environment by a socially-oriented logistics company. This work presents a case study based on the fixed delivery routes of a social logistics company and describes how the proposed system could help in the optimization of routes and the reception of orders by the elderly via ICTs thanks to the use of IPAs. Subsequent to the study, route cost savings have been observed as well as the homogeneity of the cost of the routes it can provide compared to a static and predefined fixed route planning. This study also shows how the IPAs make possible the reception of orders in real time placed by the older adults from their homes. This work is aimed at improving the sustainability of services in depopulated rural areas while saving costs for the logistics companies that perform these services.

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Damacharla, Praveen, Parashar Dhakal, Jyothi Priyanka Bandreddi, Ahmad Y. Javaid, Jennie J. Gallimore, Colin Elkin, and Vijay K. Devabhaktuni. "Novel Human-in-the-Loop (HIL) Simulation Method to Study Synthetic Agents and Standardize Human–Machine Teams (HMT)." Applied Sciences 10, no. 23 (November 25, 2020): 8390. http://dx.doi.org/10.3390/app10238390.

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This work presents a multi-year study conducted at the University of Toledo, aimed at improving human–machine teaming (HMT) methods and technologies. With the advent of artificial intelligence (AI) in 21st-century machines, collaboration between humans and machines has become highly complicated for real-time applications. The penetration of intelligent and synthetic assistants (IA/SA) in virtually every field has opened up a path to the area of HMT. When it comes to crucial tasks such as patient treatment/care, industrial production, and defense, the use of non-standardized HMT technologies may pose a risk to human lives and cost billions of taxpayer dollars. A thorough literature survey revealed that there are not many established standards or benchmarks for HMT. In this paper, we propose a method to design an HMT based on a generalized architecture. This design includes the development of an intelligent collaborative system and the human team. Followed by the identification of processes and metrics to test and validate the proposed model, we present a novel human-in-the-loop (HIL) simulation method. The effectiveness of this method is demonstrated using two controlled HMT scenarios: Emergency care provider (ECP) training and patient treatment by an experienced medic. Both scenarios include humans processing visual data and performing actions that represent real-world applications while responding to a Voice-Based Synthetic Assistant (VBSA) as a collaborator that keeps track of actions. The impact of various machines, humans, and HMT parameters is presented from the perspective of performance, rules, roles, and operational limitations. The proposed HIL method was found to assist in standardization studies in the pursuit of HMT benchmarking for critical applications. Finally, we present guidelines for designing and benchmarking HMTs based on the case studies’ results analysis.

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Sáiz-Manzanares, María Consuelo, Raúl Marticorena-Sánchez, and Javier Ochoa-Orihuel. "Effectiveness of Using Voice Assistants in Learning: A Study at the Time of COVID-19." International Journal of Environmental Research and Public Health 17, no. 15 (August 4, 2020): 5618. http://dx.doi.org/10.3390/ijerph17155618.

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The use of advanced learning technologies in a learning management system (LMS) can greatly assist learning processes, especially when used in university environments, as they promote the development of Self-Regulated learning, which increases academic performance and student satisfaction towards personal learning. One of the most innovative resources that an LMS may have is an Intelligent Personal Assistant (IPA). We worked with a sample of 109 third-grade students following Health Sciences degrees. The aims were: (1) to verify whether there will be significant differences in student access to the LMS, depending on use versus non-use of an IPA. (2) To verify whether there will be significant differences in student learning outcomes depending on use versus non-use of an IPA. (3) To verify whether there will be significant differences for student satisfaction with teaching during the COVID-19 pandemic, depending on use versus non-use of an IPA. (4) To analyze student perceptions of the usefulness of an IPA in the LMS. We found greater functionality in access to the LMS and satisfaction with teaching, especially during the health crisis, in the group of students who had used an IPA. However, both the expansion of available information and the usability of the features embedded in an IPA are still challenging issues.

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Lee, Hyerin, and Sang Woo Lee. "The Influence of the Attractiveness and Para-social Interaction of Intelligent Personal Assistant on the Product Attitude of Voice Shopping Users." Information Society & Media 21, no. 1 (April 30, 2020): 55–82. http://dx.doi.org/10.52558/ism.2020.04.21.1.55.

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Cao, Qianyu, and Hanmei Hao. "Optimization of Intelligent English Pronunciation Training System Based on Android Platform." Complexity 2021 (March 26, 2021): 1–11. http://dx.doi.org/10.1155/2021/5537101.

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Oral English, as a language tool, is not only an important part of English learning but also an essential part. For nonnative English learners, effective and meaningful voice feedback is very important. At present, most of the traditional recognition and error correction systems for oral English training are still in the theoretical stage. At the same time, the corresponding high-end experimental prototype also has the disadvantages of large and complex system. In the speech recognition technology, the traditional speech recognition technology is not perfect in recognition ability and recognition accuracy, and it relies too much on the recognition of speech content, which is easily affected by the noise environment. Based on this, this paper will develop and design a spoken English assistant pronunciation training system based on Android smartphone platform. Based on the in-depth study and analysis of spoken English speech correction algorithm and speech feedback mechanism, this paper proposes a lip motion judgment algorithm based on ultrasonic detection, which is used to assist the traditional speech recognition algorithm in double feedback judgment. In the feedback mechanism of intelligent speech training, a double benchmark scoring mechanism is introduced to comprehensively evaluate the speech of the speech trainer and correct the speaker’s speech in time. The experimental results show that the speech accuracy of the system reaches 85%, which improves the level of oral English trainers to a certain extent.

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Sagaspe, Patricia, Véronique Lespinet-Najib, Sylvie Poulette, Jean-Christophe Vasselon, Amélie Roche, Colette Fabrigoule, Thierry Le Brun, et al. "Feasibility of the cognitive assessment of nursing home residents with mild-to-moderate cognitive impairment using the intelligent voice-guided digital assistant THALIE: A pilot study." Gerontechnology 19, no. 4 (December 31, 2020): 1–9. http://dx.doi.org/10.4017/gt.2020.19.04.390.

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Kudina, Olga. "Alexa Does Not Care. Should You?" Glimpse 20 (2019): 107–15. http://dx.doi.org/10.5840/glimpse2019207.

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This article explores the ethical dimension of digital voice assistants from the angle of postphenomenology and the technological mediation approach, whereby technology plays a mediating role in the human-world relations. Digital voice assistants, such as Amazon Echo’s Alexa or Google’s Home, increasingly form an integral part of everyday life for many people. Powered by Artificial Intelligence and based on voice interaction, voice assistants promise constant accompaniment by answering any questions people might have and even managing the physical space of their homes. However, while accompanying daily lives of people, voice assistants also seamlessly redefine the way people talk, interact and perceive each other. In view of their intentionalities, such as interaction by voice, command-based model of communication and development of attachment, digital voice assistant mediate the norms of interaction beyond their immediate use, the way people perceive themselves, those around and form consequent normative expectations. The article argues that understanding how technologies, such as digital voice assistants, mediate our moral landscape forms an essential part of media literacy in the digital age.

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Dellaert, Benedict G. C., Suzanne B. Shu, Theo A. Arentze, Tom Baker, Kristin Diehl, Bas Donkers, Nathanael J. Fast, et al. "Consumer decisions with artificially intelligent voice assistants." Marketing Letters 31, no. 4 (August 17, 2020): 335–47. http://dx.doi.org/10.1007/s11002-020-09537-5.

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AbstractConsumers are widely adopting Artificially Intelligent Voice Assistants (AIVAs). AIVAs now handle many different everyday tasks and are also increasingly assisting consumers with purchasing decisions, making AIVAs a rich topic for marketing researchers. We develop a series of propositions regarding how consumer decision-making processes may change when moved from traditional online purchase environments to AI-powered voice-based dialogs, in the hopes of encouraging further academic thinking and research in this rapidly developing, high impact area of consumer-firm interaction. We also provide suggestions for marketing managers and policymakers on points to pay attention to when they respond to the proposed effects of AIVAs on consumer decisions.

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Tejaswi, Ch V. "Virtual Voice Assistant." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4097–101. http://dx.doi.org/10.22214/ijraset.2021.35868.

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This is desktop application which can assist people with basic tasks using natural language. Virtual Voice Assistants can go online and search for an answer to a user’s question. Actions can be triggered using text or voice. Voice is the key. A virtual voice assistant is a personal assistant which uses natural language processing (NLP) , voice recognition and speech synthesis to provide a service through a particular application. Natural Language Processing in short is called as NLP. It is basically a branch of artificial intelligence which mainly deals with the interaction between personal computers and human beings using the natural language. The main objective of NLP is to read, convert, understand, and make use of the human languages in a manner that is valuable. Voice recognition is a hardware device or computer software program with the potential to decode the voice of human beings. Voice recognition is usually used to operate a gadget, execute commands, or write without making use of any mouse, keyboard, or press any buttons. Artificial production of human speech is called as Speech Synthesis. A system used for this purpose is called a speech computer or speech synthesizer and can be implemented in many products of software’s and hardware’s.

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Pérès, Karine, Alfonso Zamudio-Rodriguez, Jean-Francois Dartigues, Hélène Amieva, and Stephane Lafitte. "Prospective pragmatic quasi-experimental study to assess the impact and effectiveness of an innovative large-scale public health intervention to foster healthy ageing in place: the SoBeezy program protocol." BMJ Open 11, no. 4 (April 2021): e043082. http://dx.doi.org/10.1136/bmjopen-2020-043082.

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IntroductionWith the accelerating pace of ageing, healthy ageing has become a major challenge for all societies worldwide. Based on that Healthy Ageing concept proposed by the WHO, the SoBeezy intervention has been designed through an older person-centred and integrated approach. The programme creates the environments that maximise functional ability to enable people to be and do what they value and to stay at home in best possible conditions.Methods and analysisFive levers are targeted: tackling loneliness, restoring feeling of usefulness, finding solutions to face material daily life difficulties, promoting social participation and combating digital divide. Concretely, the SoBeezy programme relies on: (1) a digital intelligent platform available on smartphone, tablet and computer, but also on a voice assistant specifically developed for people with digital divide; (2) a large solidarity network which potentially relies on everyone’s engagement through a participatory intergenerational approach, where the older persons themselves are not only service receivers but also potential contributors; (3) an engagement of local partners and stakeholders (citizens, associations, artisans and professionals). Organised as a hub, the system connects all the resources of a territory and provides to the older person the best solution to meet his demand. Through a mixed, qualitative and quantitative (before/after analyses and compared to controls) approach, the research programme will assess the impact and effectiveness on healthy ageing, the technical usage, the mechanisms of the intervention and conditions of transferability and scalability.Ethics and disseminationInserm Ethics Committee and the Comité Éthique et Scientifique pour les Recherches, les Études et les Évaluations dans le domaine de la Santé approved this research and collected data will be deposited with a suitable data archive.

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Kopitz, Linda. "Alexa, Affect, and the Algorithmic Imaginary." Screen Bodies 6, no. 1 (June 1, 2021): 1–17. http://dx.doi.org/10.3167/screen.2021.060103.

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As millions of customers across the world invite digital voice assistants into their homes, the public debate has increasingly centered on security and privacy concerns connected to the use of the device. Drawing on Tania Bucher’s work at the intersection between technology and everyday experience, this article proposes an understanding of an algorithmic imaginary of Alexa-enabled devices as explicitly nonthreatening in its ordinariness, positive potential, and gendered presence. As a case study, this article uses commercials for Alexa-enabled devices as a starting point: Instead of foregrounding the functionality and thereby the algorithmic intelligence underlying the voice assistant, these commercials focus on an affective potential as a narrative strategy to address privacy and security concerns. By connecting everyday interactions with emotional and empowering narratives, the way Alexa is portrayed as an embodied object functions as a balance to the equally public and publicized understanding of digital voice assistants as threats.

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Chattaraman, Veena, Wi-Suk Kwon, Wanda Eugene, and Juan E. Gilbert. "Developing and Validating a Naturalistic Decision Model for Intelligent Language-Based Decision Aids." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 176–77. http://dx.doi.org/10.1177/1541931213601528.

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People make mundane and critical consumption decisions every day using choice processes that are inherently constructive in nature, where preferences emerge ‘on the spot’ or ‘on the go’ using multiple strategies based on the task at hand (Bettman, Luce, & Payne, 1998; Sproule & Archer, 2000). This implies that applying a single, invariant algorithm will not solve decision problems that humans face (Tversky, Sattath, & Slovic, 1988). Instead, consumers need adaptive, multi-strategy decision aids since they shift between multiple strategies in a single decision as they acquire increasing information during the decision-making process (Bettman et al., 1998). This paper puts forth a cognitive computing approach to develop and validate a naturalistic decision model for designing language-based, mobile decision-aids (MoDA©) based on adaptive and intelligent information retrieval and multi-decision strategy use. The approach integrates established psychological theories, Elaboration Likelihood Model (ELM) and Construal Level Theory (CLT), to develop the scientific base for predicting decision-making under contingencies. ELM delineates whether human information processing is effortful or heuristic based on a person’s ability and motivation to engage in an object-relevant elaboration (Petty & Cacioppo, 1981). CLT determines whether the cognitive construal of the decision object is abstract or concrete based on psychological distance (Liberman, Trope, & Wakslak, 2007). Integrating the derivatives of these theories, the Human-Elaboration-Object-Construal (H-E-O-C) Contingency Decision Model’s central thesis is that the decision-making strategy employed by a decision-maker can be predicted by using natural language cues to infer the extent of human elaboration (low-high) on the decision and the type of knowledge (abstract-concrete) possessed on the decision object. Specifically, an extensive (vs. limited) decision strategy is likely to be employed when human elaboration revealed through natural language cues is high (vs. low). Further, an attribute-based (vs. alternative-based) strategy may be employed when the cognitive representation of the decision object is abstract (vs. concrete). Based on this theorizing, the H-E-O-C Contingency Decision Model can predict the use of four common decision strategies that systematically differ based on the amount (extensive vs. limited) and pattern (attribute- vs. alternative-based) of processing: Lexicographic or LEX (limited, attribute-based processing), Satisficing or SAT (limited, alternative-based processing), Elimination-by-Aspects or EBA (extensive, attribute-based processing), and Weighted Adding or WADD (extensive, alternative-based processing) (Bettman et al., 1998). To validate the H-E-O-C Contingency Decision Model, we conducted observational studies that simulated in-store purchase decision-making with real consumers. A total of 48 shopping sessions (n = 48) were held in a simulation home improvement retail store, and decision-making dialog between consumers and a customer service agent (trained research assistant) was recorded using wearable voice recorders. To ensure that there were fairly equal numbers of consumers who were either motivated or not to elaborate on their decisions, we created two shopping conditions – low risk (replacement AC filter purchase) and high risk (AC filter purchase to address allergy and asthma). The recorded decision dialogs were first transcribed verbatim, resulting 48 units of analysis, which were then analyzed using the grounded theory approach through open and axial coding processes (Corbin & Strauss, 1990). The open coding first identified the construal level, which was followed by axial coding to infer the decision strategy (LEX, EBA, SAT, or WADD) employed by the consumer at the initial and final stages of decision-making. This process was conducted by two coders with adequate inter-coder reliability. Two different coders coded the transcripts for the elaboration level (low vs. high) of the consumer based on specific definitions, with adequate inter-coder reliability. The H-E-O-C Contingency Decision Model proposes that high elaboration consumers will employ either WADD or EBA, whereas low elaboration consumers will employ either SAT or LEX. This proposition was supported in over 80% of the decision transcripts, offering an important validation of the framework. The main contribution of the H-E-O-C Contingency Decision Model is that it is derived from universal psychological constructs and predicts decision-making strategies that apply to many types of products and services related to healthcare, education, and finance that are characterized by attributes and alternatives. This ensures its broad applicability across a wide variety of disciplines and use cases.

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Donepudi, Praveen Kumar. "Voice Search Technology: An Overview." Engineering International 2, no. 2 (December 31, 2014): 91–102. http://dx.doi.org/10.18034/ei.v2i2.502.

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Voice search technology is not new. It's been there since Audrey came out. But now it is increasing. People are using voice assistants, and so far 420 million voice assistants have been sold. This is growing rapidly because artificial intelligence integrated with voice search technology is more accurate and faster than text search. According to Google, its accuracy has improved to 95%. In short, it's an old technology that's gaining popularity with the rise of mobile phone voice assistants. In this study, the researcher has introduced different aspects and practical implications of voice search technology.

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O'Brien, Katherine, Anna Liggett, Vanessa Ramirez‐Zohfeld, Priya Sunkara, and Lee A. Lindquist. "Voice‐Controlled Intelligent Personal Assistants to Support Aging in Place." Journal of the American Geriatrics Society 68, no. 1 (October 16, 2019): 176–79. http://dx.doi.org/10.1111/jgs.16217.

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Tregubov, Vladimir N. "The use of artificial intelligence techniques for distance learning." Izvestiya of Saratov University. New Series. Series: Philosophy. Psychology. Pedagogy 21, no. 2 (June 24, 2021): 222–27. http://dx.doi.org/10.18500/1819-7671-2021-21-2-222-227.

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The article describes some practices of voice recognition technologies based on artificial intelligence to improve the educational process in a remote format. This is relevant to modern remote education environment. The author presents an analysis of existing approaches to the use of artificial intelligence technologies in the educational process. Elements of artificial intelligence in a specialist software make the educational process more modern and convenient both for the learner and the teacher. One of the implementations for a natural user interface is a voice-user interface, which recognizes words to understand spoken commands and to answer questions, it converts the text into speech. The author has carried out an analytical review of implementations of specialized applications for Yandex assistant in which, the voice interface is used to improve children's language skills. Algorithm development of an application for Google assistant which uses voice recognition technologies and artificial intelligence for for teaching a foreign language is described. The application can be used in the educational process for teaching academic English; it also allows to increase English vocabulary, train the construction of grammatically correct scientific expressions, and memorize scientific language patterns.

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Shin, Jong-Gyu, Ga-Young Choi, Han-Jeong Hwang, and Sang-Ho Kim. "Evaluation of Emotional Satisfaction Using Questionnaires in Voice-Based Human–AI Interaction." Applied Sciences 11, no. 4 (February 22, 2021): 1920. http://dx.doi.org/10.3390/app11041920.

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With the development of artificial intelligence technology, voice-based intelligent systems (VISs), such as AI speakers and virtual assistants, are intervening in human life. VISs are emerging in a new way, called human–AI interaction, which is different from existing human–computer interaction. Using the Kansei engineering approach, we propose a method to evaluate user satisfaction during interaction between a VIS and a user-centered intelligent system. As a user satisfaction evaluation method, a VIS comprising four types of design parameters was developed. A total of 23 subjects were considered for interaction with the VIS, and user satisfaction was measured using Kansei words (KWs). The questionnaire scores collected through KWs were analyzed using exploratory factor analysis. ANOVA was used to analyze differences in emotion. On the “pleasurability” and “reliability” axes, it was confirmed that among the four design parameters, “sentence structure of the answer” and “number of trials to get the right answer for a question” affect the emotional satisfaction of users. Four satisfaction groups were derived according to the level of the design parameters. This study can be used as a reference for conducting an integrated emotional satisfaction assessment using emotional metrics such as biosignals and facial expressions.

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Richer, Robert, Nan Zhao, Bjoern M. Eskofier, and Joseph A. Paradiso. "Exploring Smart Agents for the Interaction with Multimodal Mediated Environments." Multimodal Technologies and Interaction 4, no. 2 (June 6, 2020): 27. http://dx.doi.org/10.3390/mti4020027.

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After conversational agents have been made available to the broader public, we speculate that applying them as a mediator for adaptive environments reduces control complexity and increases user experience by providing a more natural interaction. We implemented and tested four agents, each of them differing in their system intelligence and input modality, as personal assistants for Mediated Atmospheres, an adaptive smart office prototype. They were evaluated in a user study ( N = 33 ) to collect subjective and objective measures. Results showed that a smartphone application was the most favorable system, followed by conversational text and voice agents that were perceived as being more engaging and intelligent than a non-conversational voice agent. Significant differences were observed between native and non-native speakers in both subjective and objective measures. Our findings reveal the potential of conversational agents for the interaction with adaptive environments to reduce work and information overload.

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Seaborn, Katie, Norihisa P. Miyake, Peter Pennefather, and Mihoko Otake-Matsuura. "Voice in Human–Agent Interaction." ACM Computing Surveys 54, no. 4 (May 2021): 1–43. http://dx.doi.org/10.1145/3386867.

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Social robots, conversational agents, voice assistants, and other embodied AI are increasingly a feature of everyday life. What connects these various types of intelligent agents is their ability to interact with people through voice. Voice is becoming an essential modality of embodiment, communication, and interaction between computer-based agents and end-users. This survey presents a meta-synthesis on agent voice in the design and experience of agents from a human-centered perspective: voice-based human–agent interaction (vHAI). Findings emphasize the social role of voice in HAI as well as circumscribe a relationship between agent voice and body, corresponding to human models of social psychology and cognition. Additionally, changes in perceptions of and reactions to agent voice over time reveals a generational shift coinciding with the commercial proliferation of mobile voice assistants. The main contributions of this work are a vHAI classification framework for voice across various agent forms, contexts, and user groups, a critical analysis grounded in key theories, and an identification of future directions for the oncoming wave of vocal machines.

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Dale, Robert. "Voice assistance in 2019." Natural Language Engineering 26, no. 1 (December 27, 2019): 129–36. http://dx.doi.org/10.1017/s1351324919000640.

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AbstractThe end of the calendar year always seems like a good time to pause for breath and reflect on what’s been happening over the last 12 months, and that’s as true in the world of commercial NLP as it is in any other domain. In particular, 2019 has been a busy year for voice assistance, thanks to the focus placed on this area by all the major technology players. So, we take this opportunity to review a number of key themes that have defined recent developments in the commercialization of voice technology.

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Han, Sangyeal, and Heetae Yang. "Understanding adoption of intelligent personal assistants." Industrial Management & Data Systems 118, no. 3 (April 9, 2018): 618–36. http://dx.doi.org/10.1108/imds-05-2017-0214.

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Purpose The purpose of this paper is to develop a comprehensive research model that can explain customers’ continuance intentions to adopt and use intelligent personal assistants (IPAs). Design/methodology/approach This study proposes and validates a new theoretical model that extends the parasocial relationship (PSR) theory. Partial least squares analysis is employed to test the research model and corresponding hypotheses on data collected from 304 survey samples. Findings Interpersonal attraction (task attraction, social attraction, and physical attraction) and security/privacy risk are important factors affecting the adoption of IPAs. Research limitations/implications First, this is the first empirical study to examine user acceptance of IPAs. Second, to the authors’ knowledge, no research has been conducted to test the role of PSR in the context of IPAs. Third, this study verified the robustness of the proposed model by introducing new antecedents reflecting risk-related attributes, which has not been investigated in prior PSR research. But this study has limitations that future research may address. First, key findings of this research are based only on data from users in the USA. Second, individual differences among the survey respondents were not examined. Practical implications To increase the adoption of IPAs, manufacturers should focus on developing “human-like” and “professional” assistants, in consideration of the important role of PSR and task attraction. R&D should continuously strive to realize artificial intelligence technology advances so that IPAs can better recognize the user’s voice and speak naturally like a person. Collaboration with third-party companies or individual developers is essential in this field, as manufacturers are unable to independently develop applications that support the specific tasks of various industries. It is also necessary to enhance IPA device design and its user interface to enhance physical attraction. Originality/value This study is the first empirical attempt to examine user acceptance of IPAs, as most of the prior literature has concerned analysis of usage patterns or technical features.

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Pradhan, Alisha, Amanda Lazar, and Leah Findlater. "Use of Intelligent Voice Assistants by Older Adults with Low Technology Use." ACM Transactions on Computer-Human Interaction 27, no. 4 (September 25, 2020): 1–27. http://dx.doi.org/10.1145/3373759.

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Sharevski, Filipo, Peter Jachim, Paige Treebridge, Audrey Li, Adam Babin, and Christopher Adadevoh. "Meet Malexa, Alexa’s malicious twin: Malware-induced misperception through intelligent voice assistants." International Journal of Human-Computer Studies 149 (May 2021): 102604. http://dx.doi.org/10.1016/j.ijhcs.2021.102604.

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Jadczyk, Tomasz, Wojciech Wojakowski, Michal Tendera, Timothy D. Henry, Gregory Egnaczyk, and Satya Shreenivas. "Artificial Intelligence Can Improve Patient Management at the Time of a Pandemic: The Role of Voice Technology." Journal of Medical Internet Research 23, no. 5 (May 25, 2021): e22959. http://dx.doi.org/10.2196/22959.

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Artificial intelligence–driven voice technology deployed on mobile phones and smart speakers has the potential to improve patient management and organizational workflow. Voice chatbots have been already implemented in health care–leveraging innovative telehealth solutions during the COVID-19 pandemic. They allow for automatic acute care triaging and chronic disease management, including remote monitoring, preventive care, patient intake, and referral assistance. This paper focuses on the current clinical needs and applications of artificial intelligence–driven voice chatbots to drive operational effectiveness and improve patient experience and outcomes.

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Linan-Reyes, Matias, Joaquin Garrido-Zafra, Aurora Gil-de-Castro, and Antonio Moreno-Munoz. "Energy Management Expert Assistant, a New Concept." Sensors 21, no. 17 (September 2, 2021): 5915. http://dx.doi.org/10.3390/s21175915.

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In recent years, interest in home energy management systems (HEMS) has grown significantly, as well as the development of Voice Assistants that substantially increase home comfort. This paper presents a novel merging of HEMS with the Assistant paradigm. The combination of both concepts has allowed the creation of a high-performance and easy-to-manage expert system (ES). It has been developed in a framework that includes, on the one hand, the efficient energy management functionality boosted with an Internet of Things (IoT) platform, where artificial intelligence (AI) and big data treatment are blended, and on the other hand, an assistant that interacts both with the user and with the HEMS itself. The creation of this ES has made it possible to optimize consumption levels, improve security, efficiency, comfort, and user experience, as well as home security (presence simulation or security against intruders), automate processes, optimize resources, and provide relevant information to the user facilitating decision making, all based on a multi-objective optimization (MOP) problem model. This paper presents both the scheme and the results obtained, the synergies generated, and the conclusions that can be drawn after 24 months of operation.

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Gulati, Ujjwal, Ishaan, and Rajeshwar Dass. "Intelligent Car with Voice Assistance and Obstacle Detector to Aid the Disabled." Procedia Computer Science 167 (2020): 1732–38. http://dx.doi.org/10.1016/j.procs.2020.03.383.

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Saratha Devi, G., Vinothini G, Janani Devi.C, and Jeyachandrakala C. "An enhanced voice based chatbot using artificial intelligence." International Journal of Engineering & Technology 7, no. 1.3 (December 31, 2017): 118. http://dx.doi.org/10.14419/ijet.v7i1.3.9669.

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The chatbot application developed using various programming languages with user interface to send input and receive response contains certain boundaries and limitations. This paper proposes a chatbot application for assisting the information regarding the colleges with the use of artificial intelligence. This system has been embedded artificial intelligence to help the user to resolve the questions by providing a human way interactions to identify the sentences and making a decision itself as response to answer a question overcoming the use of programming languages.

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Lee, So-Ra, Hyeon-Kyeong Shin, Se-Ah An, and Kyeonah Yu. "Development of Voice Assistant Apps for Low Vision People Based on Artificial Intelligence." Journal of Digital Contents Society 21, no. 2 (February 29, 2020): 277–84. http://dx.doi.org/10.9728/dcs.2020.21.2.277.

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Journal articles on the topic 'Intelligent Voice Assistant'

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