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Journal articles on the topic 'Spoken Dialog System'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Kim, A.-Yeong, Hyun-Je Song, and Seong-Bae Park. "A Two-Step Neural Dialog State Tracker for Task-Oriented Dialog Processing." Computational Intelligence and Neuroscience 2018 (October 18, 2018): 1–11. http://dx.doi.org/10.1155/2018/5798684.

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Dialog state tracking in a spoken dialog system is the task that tracks the flow of a dialog and identifies accurately what a user wants from the utterance. Since the success of a dialog is influenced by the ability of the system to catch the requirements of the user, accurate state tracking is important for spoken dialog systems. This paper proposes a two-step neural dialog state tracker which is composed of an informativeness classifier and a neural tracker. The informativeness classifier which is implemented by a CNN first filters out noninformative utterances in a dialog. Then, the neural tracker estimates dialog states from the remaining informative utterances. The tracker adopts the attention mechanism and the hierarchical softmax for its performance and fast training. To prove the effectiveness of the proposed model, we do experiments on dialog state tracking in the human-human task-oriented dialogs with the standard DSTC4 data set. Our experimental results prove the effectiveness of the proposed model by showing that the proposed model outperforms the neural trackers without the informativeness classifier, the attention mechanism, or the hierarchical softmax.

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2

Demberg, Vera, Andi Winterboer, and Johanna D. Moore. "A Strategy for Information Presentation in Spoken Dialog Systems." Computational Linguistics 37, no. 3 (September 2011): 489–539. http://dx.doi.org/10.1162/coli_a_00064.

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In spoken dialog systems, information must be presented sequentially, making it difficult to quickly browse through a large number of options. Recent studies have shown that user satisfaction is negatively correlated with dialog duration, suggesting that systems should be designed to maximize the efficiency of the interactions. Analysis of the logs of 2,000 dialogs between users and nine different dialog systems reveals that a large percentage of the time is spent on the information presentation phase, thus there is potentially a large pay-off to be gained from optimizing information presentation in spoken dialog systems. This article proposes a method that improves the efficiency of coping with large numbers of diverse options by selecting options and then structuring them based on a model of the user's preferences. This enables the dialog system to automatically determine trade-offs between alternative options that are relevant to the user and present these trade-offs explicitly. Multiple attractive options are thereby structured such that the user can gradually refine her request to find the optimal trade-off. To evaluate and challenge our approach, we conducted a series of experiments that test the effectiveness of the proposed strategy. Experimental results show that basing the content structuring and content selection process on a user model increases the efficiency and effectiveness of the user's interaction. Users complete their tasks more successfully and more quickly. Furthermore, user surveys revealed that participants found that the user-model based system presents complex trade-offs understandably and increases overall user satisfaction. The experiments also indicate that presenting users with a brief overview of options that do not fit their requirements significantly improves the user's overview of available options, also making them feel more confident in having been presented with all relevant options.

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Huang, Ting-Hao, Walter Lasecki, and Jeffrey Bigham. "Guardian: A Crowd-Powered Spoken Dialog System for Web APIs." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 3 (September 23, 2015): 62–71. http://dx.doi.org/10.1609/hcomp.v3i1.13237.

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Natural language dialog is an important and intuitive way for people to access information and services. However, current dialog systems are limited in scope, brittle to the richness of natural language, and expensive to produce. This paper introduces Guardian, a crowd-powered framework that wraps existing Web APIs into immediately usable spoken dialog systems. Guardian takes as input the Web API and desired task, and the crowd determines the parameters necessary to complete it, how to ask for them, and interprets the responses from the API. The system is structured so that, over time, it can learn to take over for the crowd. This hybrid systems approach will help make dialog systems both more general and more robust going forward.

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4

Griol, David, José Antonio Iglesias, Agapito Ledezma, and Araceli Sanchis. "A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems." International Journal of Neural Systems 26, no. 01 (January 5, 2016): 1650002. http://dx.doi.org/10.1142/s0129065716500027.

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This paper proposes a statistical framework to develop user-adapted spoken dialog systems. The proposed framework integrates two main models. The first model is used to predict the user’s intention during the dialog. The second model uses this prediction and the history of dialog up to the current moment to predict the next system response. This prediction is performed with an ensemble-based classifier trained for each of the tasks considered, so that a better selection of the next system can be attained weighting the outputs of these specialized classifiers. The codification of the information and the definition of data structures to store the data supplied by the user throughout the dialog makes the estimation of the models from the training data and practical domains manageable. We describe our proposal and its application and detailed evaluation in a practical spoken dialog system.

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Mestrovic, Ana, Luka Bernic, Miran Pobar, Sanda Martincic-Ipsic, and Ivo Ipsic. "A Croatian Weather Domain Spoken Dialog System Prototype." Journal of Computing and Information Technology 18, no. 4 (2010): 309. http://dx.doi.org/10.2498/cit.1001916.

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Ip�i�, Ivo, and Nikola Pave�i�. "An Overview of the Slovenian Spoken Dialog System." Journal of Computing and Information Technology 10, no. 4 (2002): 295. http://dx.doi.org/10.2498/cit.2002.04.04.

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7

Stoyanchev, Svetlana, and Amanda J. Stent. "Concept Type Prediction and Responsive Adaptation in a Dialogue System." Dialogue & Discourse 3, no. 1 (February 10, 2012): 1–31. http://dx.doi.org/10.5087/dad.2012.101.

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Responsive adaptation in spoken dialog systems involves a change in dialog system behavior in response to a user or a dialog situation. In this paper we address responsive adaptation in the automatic speech recognition (ASR) module of a spoken dialog system. We hypothesize that information about the content of a user utterance may help improve speech recognition for the utterance. We use a two-step process to test this hypothesis: first, we automatically predict the task-relevant concept types likely to be present in a user utterance using features from the dialog context and from the output of first-pass ASR of the utterance; and then, we adapt the ASR's language model to the predicted content of the user's utterance and run a second pass of ASR. We show that: (1) it is possible to achieve high accuracy in determining presence or absence of particular concept types in a post-confirmation utterance; and (2) 2-pass speech recognition with concept type classification and language model adaptation can lead to improved speech recognition performance for post-confirmation utterances.

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Ou, Yang-Yen, Ta-Wen Kuan, Anand Paul, Jhing-Fa Wang, and An-Chao Tsai. "Spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition." Frontiers of Computer Science 11, no. 3 (June 2017): 429–43. http://dx.doi.org/10.1007/s11704-016-6190-2.

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9

Nagai, Akito. "Spoken dialog system capable of performing natural interactive access." Journal of the Acoustical Society of America 112, no. 1 (2002): 22. http://dx.doi.org/10.1121/1.1500923.

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10

DELISLE, SYLVAIN, BERNARD MOULIN, and TERRY COPECK. "Surface-marker-based dialog modelling: A progress report on the MAREDI project." Natural Language Engineering 9, no. 4 (November 25, 2003): 325–63. http://dx.doi.org/10.1017/s1351324903003231.

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Most information systems that deal with natural language texts do not tolerate much deviation from their idealized and simplified model of language. Spoken dialog is notoriously ungrammatical, however. Because the MAREDI project focuses in particular on the automatic analysis of scripted dialogs, we needed to develop a robust capacity to analyze transcribed spoken language. This paper summarizes the current state of our work. It presents the main elements of our approach, which is based on exploiting surface markers as the best route to the semantics of the conversation modelled. We highlight the foundations of our particular conversational model, and give an overview of the MAREDI system. We then discuss its three key modules, a connectionist network to recognise speech acts, a robust syntactic analyzer, and a semantic analyzer.

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11

Misu, Teruhisa, Antoine Raux, Rakesh Gupta, and Ian Lane. "Situated language understanding for a spoken dialog system within vehicles." Computer Speech & Language 34, no. 1 (November 2015): 186–200. http://dx.doi.org/10.1016/j.csl.2015.02.002.

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12

Lee, Donghyeon, Minwoo Jeong, Kyungduk Kim, Seonghan Ryu, and Gary Geunbae Lee. "Unsupervised Spoken Language Understanding for a Multi-Domain Dialog System." IEEE Transactions on Audio, Speech, and Language Processing 21, no. 11 (November 2013): 2451–64. http://dx.doi.org/10.1109/tasl.2013.2280212.

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13

Williams, Jason D., Antoine Raux, and Matthew Henderson. "The Dialog State Tracking Challenge Series: A Review." Dialogue & Discourse 7, no. 3 (April 15, 2016): 4–33. http://dx.doi.org/10.5087/dad.2016.301.

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In a spoken dialog system, dialog state tracking refers to the task of correctly inferring the state of the conversation -- such as the user's goal -- given all of the dialog history up to that turn. Dialog state tracking is crucial to the success of a dialog system, yet until recently there were no common resources, hampering progress. The Dialog State Tracking Challenge series of 3 tasks introduced the first shared testbed and evaluation metrics for dialog state tracking, and has underpinned three key advances in dialog state tracking: the move from generative to discriminative models; the adoption of discriminative sequential techniques; and the incorporation of the speech recognition results directly into the dialog state tracker. This paper reviews this research area, covering both the challenge tasks themselves and summarizing the work they have enabled.

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14

Dong Yu and A. Acero. "Semiautomatic improvements of system-initiative spoken dialog applications using interactive clustering." IEEE Transactions on Speech and Audio Processing 13, no. 5 (September 2005): 661–71. http://dx.doi.org/10.1109/tsa.2005.851876.

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15

Yang, Zhaojun, Gina-Anne Levow, and Helen Meng. "Predicting User Satisfaction in Spoken Dialog System Evaluation With Collaborative Filtering." IEEE Journal of Selected Topics in Signal Processing 6, no. 8 (December 2012): 971–81. http://dx.doi.org/10.1109/jstsp.2012.2229965.

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16

Strik, Helmer, Albert Russel, Henk Van Den Heuvel, Catia Cucchiarini, and Lou Boves. "A spoken dialog system for the Dutch public transport information service." International Journal of Speech Technology 2, no. 2 (December 1997): 121–31. http://dx.doi.org/10.1007/bf02208824.

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17

Jung, Sangkeun, Cheongjae Lee, Seokhwan Kim, and Gary Geunbae Lee. "DialogStudio: A workbench for data-driven spoken dialog system development and management." Speech Communication 50, no. 8-9 (August 2008): 697–715. http://dx.doi.org/10.1016/j.specom.2008.04.003.

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18

Kitaoka, Norihide, Naoko Kakutani, and Seiichi Nakagawa. "Detection and recognition of correction utterances on misrecognition of spoken dialog system." Systems and Computers in Japan 36, no. 11 (2005): 24–33. http://dx.doi.org/10.1002/scj.20341.

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19

Chiba, Yuya, and Akinori Ito. "Estimating a User's Internal State before the First Input Utterance." Advances in Human-Computer Interaction 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/865362.

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This paper describes a method for estimating the internal state of a user of a spoken dialog system before his/her first input utterance. When actually using a dialog-based system, the user is often perplexed by the prompt. A typical system provides more detailed information to a user who is taking time to make an input utterance, but such assistance is nuisance if the user is merely considering how to answer the prompt. To respond appropriately, the spoken dialog system should be able to consider the user’s internal state before the user’s input. Conventional studies on user modeling have focused on the linguistic information of the utterance for estimating the user’s internal state, but this approach cannot estimate the user’s state until the end of the user’s first utterance. Therefore, we focused on the user’s nonverbal output such as fillers, silence, or head-moving until the beginning of the input utterance. The experimental data was collected on a Wizard of Oz basis, and the labels were decided by five evaluators. Finally, we conducted a discrimination experiment with the trained user model using combined features. As a three-class discrimination result, we obtained about 85% accuracy in an open test.

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20

Leuski, Anton, and David Traum. "NPCEditor: Creating Virtual Human Dialogue Using Information Retrieval Techniques." AI Magazine 32, no. 2 (March 16, 2011): 42. http://dx.doi.org/10.1609/aimag.v32i2.2347.

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NPCEditor is a system for building a natural language processing component for virtual humans capable of engaging a user in spoken dialog on a limited domain. It uses statistical language classiﬁcation technology for mapping from a user’s text input to system responses. NPCEditor provides a user-friendly editor for creating effective virtual humans quickly. It has been deployed as a part of various virtual human systems in several applications.

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21

Leuski, Anton, and David Traum. "Practical Language Processing for Virtual Humans." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 2 (October 7, 2021): 1740–47. http://dx.doi.org/10.1609/aaai.v24i2.18806.

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NPCEditor is a system for building a natural language processing component for virtual humans capable of engaging a user in spoken dialog on a limited domain. It uses a statistical language classification technology for mapping from user's text input to system responses. NPCEditor provides a user-friendly editor for creating effective virtual humans quickly. It has been deployed as a part of various virtual human systems in several applications.

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22

Nishimura, Ryota, and Seiichi Nakagawa. "A spoken dialog system for spontaneous conversations considering response timing and response type." IEEJ Transactions on Electrical and Electronic Engineering 6, S1 (November 29, 2010): S17—S26. http://dx.doi.org/10.1002/tee.20616.

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23

Weigelt, Sebastian, Tobias Hey, and Walter F. Tichy. "Context Model Acquisition from Spoken Utterances." International Journal of Software Engineering and Knowledge Engineering 27, no. 09n10 (November 2017): 1439–53. http://dx.doi.org/10.1142/s0218194017400058.

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Current systems with spoken language interfaces do not leverage contextual information. Therefore, they struggle with understanding speakers’ intentions. We propose a system that creates a context model from user utterances to overcome this lack of information. It comprises eight types of contextual information organized in three layers: individual, conceptual, and hierarchical. We have implemented our approach as a part of the project PARSE. It aims at enabling laypersons to construct simple programs by dialog. Our implementation incrementally generates context including occurring entities and actions as well as their conceptualizations, state transitions, and other types of contextual information. Its analyses are knowledge- or rule-based (depending on the context type), but we make use of many well-known probabilistic NLP techniques. In a user study we have shown the feasibility of our approach, achieving [Formula: see text] scores from 72% up to 98% depending on the type of contextual information. The context model enables us to resolve complex identity relations. However, quantifying this effect is subject to future work. Likewise, we plan to investigate whether our context model is useful for other language understanding tasks, e.g. anaphora resolution, topic analysis, or correction of automatic speech recognition errors.

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24

Boteanu, Adrian, and Sonia Chernova. "Modeling Topics in User Dialog for Interactive Tablet Media." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 8, no. 5 (June 30, 2021): 2–8. http://dx.doi.org/10.1609/aiide.v8i5.12573.

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In this paper, we present a set of crowdsourcing and data processing techniques for annotating, segmenting and analyzing spoken dialog data to track topics of discussion between multiple users. Specifically, our system records the dialog between the parent and child as they interact with a reading game on a tablet, crowdsources the audio data to obtain transcribed text, and models topics of discussion from speech transcription using ConceptNet, a freely available commonsense knowledge base. We present preliminary results evaluating our technique using dialog collected using an interactive reading game for children 3-5 years of age. We successfully demonstrate the ability to form discussion topics by grouping words with similar meaning. The presented approach is entirely domain independent and in future work can be applied to a broad range of interactive entertainment applications, such as mobile devices, tablets and games.

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25

Wu, Chung-Hsien, Gwo-Lang Yan, and Chien-Liang Lin. "Speech act modeling in a spoken dialog system using a fuzzy fragment-class Markov model." Speech Communication 38, no. 1-2 (September 2002): 183–99. http://dx.doi.org/10.1016/s0167-6393(01)00052-8.

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26

Phaladi, Amanda, and Thipe Modipa. "The Evaluation of a Code-Switched Sepedi-English Automatic Speech Recognition System." International Journal on Cybernetics & Informatics 13, no. 2 (March 10, 2024): 33–44. http://dx.doi.org/10.5121/ijci.2024.130203.

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Speech technology is a field that encompasses various techniques and tools used to enable machines to interact with speech, such as automatic speech recognition (ASR), spoken dialog systems, and others, allowing a device to capture spoken words through a microphone from a human speaker. End-to-end approaches such as Connectionist Temporal Classification (CTC) and attention-based methods are the most used for the development of ASR systems. However, these techniques were commonly used for research and development for many high-resourced languages with large amounts of speech data for training and evaluation, leaving low-resource languages relatively underdeveloped. While the CTC method has been successfully used for other languages, its effectiveness for the Sepedi language remains uncertain. In this study, we present the evaluation of the SepediEnglish code-switched automatic speech recognition system. This end-to-end system was developed using the Sepedi Prompted Code Switching corpus and the CTC approach. The performance of the system was evaluated using both the NCHLT Sepedi test corpus and the Sepedi Prompted Code Switching corpus. The model produced the lowest WER of 41.9%, however, the model faced challenges in recognizing the Sepedi only text

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27

Taghezout, Noria. "An Agent-Based Dialog System for Adaptive and Multimodal Interface: A Case Study." Advanced Materials Research 217-218 (March 2011): 578–83. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.578.

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Graphical Interfaces using an agent-based dialog can handle errors and interruptions, and dynamically adapts to the current context and situation, the needs of the task performed, and the user model. This is especially true for the design of multimodal interfaces, where interaction designers need to physically explore and prototype new interaction modalities and therefore require development environments that especially support the interactivity and the dynamic of this creative development process. We argue that, in the domain of sophisticated human-machine interfaces, we can make use of the increasing tendency to design such interfaces as independent agents that themselves engage in an interactive dialogue (both graphical and linguistic) with their users. This paper focuses on the implementation of a flexible and robust dialogue system which integrates emotions and other influencing parameters in the dialogue flow. In order to achieve a higher degree of adaptability and multimodality, we present Spoken Language Dialogue System (SLDS) architecture. The manufacturing process of the oil plant (GLZ: Gas Liquefying Zone), is selected as an application domain in this study

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28

Wermter, S., and V. Weber. "SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks." Journal of Artificial Intelligence Research 6 (January 1, 1997): 35–85. http://dx.doi.org/10.1613/jair.282.

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Previous approaches of analyzing spontaneously spoken language often have been based on encoding syntactic and semantic knowledge manually and symbolically. While there has been some progress using statistical or connectionist language models, many current spoken- language systems still use a relatively brittle, hand-coded symbolic grammar or symbolic semantic component. In contrast, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, learned and flat analysis. In this paper, we focus on a detailed description of SCREEN's architecture, the flat syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the influence of noisy or incomplete input. The main result of this paper is that flat representations allow more robust processing of spontaneous spoken language than deeply structured representations. In particular, we show how the fault-tolerance and learning capability of connectionist networks can support a flat analysis for providing more robust spoken-language processing within an overall hybrid symbolic/connectionist framework.

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Misu, Teruhisa. "Situated reference resolution using visual saliency and crowdsourcing-based priors for a spoken dialog system within vehicles." Computer Speech & Language 48 (March 2018): 1–14. http://dx.doi.org/10.1016/j.csl.2017.09.001.

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LAMEL, LORI, WOLFGANG MINKER, and PATRICK PAROUBEK. "Towards best practice in the development and evaluation of speech recognition components of a spoken language dialog system." Natural Language Engineering 6, no. 3&4 (September 2000): 305–22. http://dx.doi.org/10.1017/s1351324900002515.

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Zhiyong Wu, H. M. Meng, Hongwu Yang, and Lianhong Cai. "Modeling the Expressivity of Input Text Semantics for Chinese Text-to-Speech Synthesis in a Spoken Dialog System." IEEE Transactions on Audio, Speech, and Language Processing 17, no. 8 (November 2009): 1567–76. http://dx.doi.org/10.1109/tasl.2009.2023161.

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32

Ramanarayanan, Vikram, David Suendermann-Oeft, Patrick Lange, Alexei V. Ivanov, Keelan Evanini, Zhou Yu, Eugene Tsuprun, and Yao Qian. "Bootstrapping Development of a Cloud-Based Spoken Dialog System in the Educational Domain From Scratch Using Crowdsourced Data." ETS Research Report Series 2016, no. 1 (May 24, 2016): 1–7. http://dx.doi.org/10.1002/ets2.12105.

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33

Lobo, Joana, Liliana Ferreira, and Aníbal JS Ferreira. "CARMIE." International Journal of E-Health and Medical Communications 8, no. 4 (October 2017): 21–37. http://dx.doi.org/10.4018/ijehmc.2017100102.

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The incidence of chronic diseases is increasing and monitoring patients in a home environment is recommended. Noncompliance with prescribed medication regimens is a concern, especially among older people. Heart failure is a chronic disease that requires patients to follow strict medication plans permanently. With the objective of helping these patients managing information about their medicines and increasing adherence, the personal medication advisor CARMIE was developed as a conversational agent capable of interacting, in Portuguese, with users through spoken natural language. The system architecture is based on a language parser, a dialog manager, and a language generator, integrated with already existing tools for speech recognition and synthesis. All modules work together and interact with the user through an Android application, supporting users to manage information about their prescribed medicines. The authors also present a preliminary usability study and further considerations on CARMIE.

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López-Cózar, Ramón, Zoraida Callejas, David Griol, and José F. Quesada. "Review of spoken dialogue systems." Loquens 1, no. 2 (December 30, 2014): e012. http://dx.doi.org/10.3989/loquens.2014.012.

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35

Lee, Cheong-Jae, Sang-Keun Jung, Kyung-Duk Kim, Dong-Hyeon Lee, and Gary Geun-Bae Lee. "Recent Approaches to Dialog Management for Spoken Dialog Systems." Journal of Computing Science and Engineering 4, no. 1 (March 31, 2010): 1–22. http://dx.doi.org/10.5626/jcse.2010.4.1.001.

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Kunc, Ladislav, Zdenek Míkovec, and Pavel Slavík. "Avatar and Dialog Turn-Yielding Phenomena." International Journal of Technology and Human Interaction 9, no. 2 (April 2013): 66–88. http://dx.doi.org/10.4018/jthi.2013040105.

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Turn-taking and turn-yielding phenomena in dialogs receive increasing attention nowadays. A growing number of spoken dialog systems inspire application designers to humanize people’s interaction experience with computers. The knowledge of psychology in discourse structure could be helpful in this effort. In this paper the authors explore effectiveness of selected visual and vocal turn-yielding cues in dialog systems using synthesized speech and an avatar. The aim of this work is to detect the role of visual and vocal cues on dialog turn-change judgment using a conversational agent. The authors compare and study the cues in two experiments. Findings of those experiments suggest that the selected visual turn-yielding cues are more effective than the vocal cues in increasing correct judgment of dialog turn-change. Vocal cues in the experiment show quite poor results and the conclusion discusses possible explanations of that.

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Sarikaya, R., Yuqing Gao, M. Picheny, and H. Erdogan. "Semantic confidence measurement for spoken dialog systems." IEEE Transactions on Speech and Audio Processing 13, no. 4 (July 2005): 534–45. http://dx.doi.org/10.1109/tsa.2005.848879.

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38

Hakkani-Tur, Dilek Z. "Active learning process for spoken dialog systems." Journal of the Acoustical Society of America 125, no. 1 (2009): 587. http://dx.doi.org/10.1121/1.3074493.

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Griol, David, Zoraida Callejas, Ramón López-Cózar, and Giuseppe Riccardi. "A domain-independent statistical methodology for dialog management in spoken dialog systems." Computer Speech & Language 28, no. 3 (May 2014): 743–68. http://dx.doi.org/10.1016/j.csl.2013.09.002.

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40

Young, Steve, Milica Gasic, Blaise Thomson, and Jason D. Williams. "POMDP-Based Statistical Spoken Dialog Systems: A Review." Proceedings of the IEEE 101, no. 5 (May 2013): 1160–79. http://dx.doi.org/10.1109/jproc.2012.2225812.

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Witt, Silke M., Walter Rolandi, Elaine Zuber, Ted Brooks, Araceli Master, Rebecca Loose, and James Hubbell. "Optimizing Successful Turn-taking in Spoken Dialog Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 54, no. 19 (September 2010): 1425–29. http://dx.doi.org/10.1177/154193121005401915.

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42

Oh, Alice H., and Alexander I. Rudnicky. "Stochastic natural language generation for spoken dialog systems." Computer Speech & Language 16, no. 3-4 (July 2002): 387–407. http://dx.doi.org/10.1016/s0885-2308(02)00012-8.

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Witt, Silke. "Modeling user response timings in spoken dialog systems." International Journal of Speech Technology 18, no. 2 (November 30, 2014): 231–43. http://dx.doi.org/10.1007/s10772-014-9265-1.

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Souvignier, B., A. Kellner, B. Rueber, H. Schramm, and F. Seide. "The thoughtful elephant: strategies for spoken dialog systems." IEEE Transactions on Speech and Audio Processing 8, no. 1 (2000): 51–62. http://dx.doi.org/10.1109/89.817453.

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ITO, A., T. OBA, T. KONASHI, M. SUZUKI, and S. MAKINO. "Selection of Optimum Vocabulary and Dialog Strategy for Noise-Robust Spoken Dialog Systems." IEICE Transactions on Information and Systems E91-D, no. 3 (March 1, 2008): 538–48. http://dx.doi.org/10.1093/ietisy/e91-d.3.538.

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Williams, Jason D., and Steve Young. "Partially observable Markov decision processes for spoken dialog systems." Computer Speech & Language 21, no. 2 (April 2007): 393–422. http://dx.doi.org/10.1016/j.csl.2006.06.008.

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Bohus, Dan, Eric Horvitz, Takayuki Kanda, Bilge Mutlu, and Antoine Raux. "Introduction to the Special Issue on Dialog with Robots." AI Magazine 32, no. 4 (December 16, 2011): 15–16. http://dx.doi.org/10.1609/aimag.v32i4.2375.

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This special issue of AI Magazine on dialog with robots brings together a collection of articles on situated dialog. The contributing authors have been working in interrelated fields of human-robot interaction, dialog systems, virtual agents, and other related areas and address core concepts in spoken dialog with embodied robots or agents. Several of the contributors participated in the AAAI Fall Symposium on Dialog with Robots, held in November 2010, and several articles in this issue are extensions of work presented there. Others include invited contributions. The articles in this collection address diverse aspects of dialog with robots, but are unified in addressing opportunities with spoken language interaction, physical embodiment, and enriched representations of context.

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Ward, Nigel G., and David DeVault. "Challenges in Building Highly-Interactive Dialog Systems." AI Magazine 37, no. 4 (January 17, 2017): 7–18. http://dx.doi.org/10.1609/aimag.v37i4.2687.

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Spoken dialog researchers have recently demonstrated highly-interactive systems in several domains. This paper considers how to build on these advances to make systems more robust, easier to develop, and more scientifically significant. We identify key challenges whose solution would lead to improvements in dialog systems and beyond.

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LEE, Sungjin, Hyungjong NOH, Jonghoon LEE, Kyusong LEE, and Gary Geunbae LEE. "Foreign Language Tutoring in Oral Conversations Using Spoken Dialog Systems." IEICE Transactions on Information and Systems E95.D, no. 5 (2012): 1216–28. http://dx.doi.org/10.1587/transinf.e95.d.1216.

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Griol, David, Lluís F. Hurtado, Encarna Segarra, and Emilio Sanchis. "A statistical approach to spoken dialog systems design and evaluation." Speech Communication 50, no. 8-9 (August 2008): 666–82. http://dx.doi.org/10.1016/j.specom.2008.04.001.

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