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Journal articles on the topic 'Signal processing; Voice recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Hu, J., C. C. Cheng, and W. H. Liu. "Processing of speech signals using a microphone array for intelligent robots." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 219, no. 2 (2005): 133–43. http://dx.doi.org/10.1243/095965105x9461.

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For intelligent robots to interact with people, an efficient human-robot communication interface is very important (e.g. voice command). However, recognizing voice command or speech represents only part of speech communication. The physics of speech signals includes other information, such as speaker direction. Secondly, a basic element of processing the speech signal is recognition at the acoustic level. However, the performance of recognition depends greatly on the reception. In a noisy environment, the success rate can be very poor. As a result, prior to speech recognition, it is important

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2

Uzdy, Z. "Human speaker recognition performance of LPC voice processors." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 3 (1985): 752–53. http://dx.doi.org/10.1109/tassp.1985.1164606.

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M Tasbolatov, N. Mekebayev, O. Mamyrbayev, M. Turdalyuly, D. Oralbekova,. "Algorithms and architectures of speech recognition systems." Psychology and Education Journal 58, no. 2 (2021): 6497–501. http://dx.doi.org/10.17762/pae.v58i2.3182.

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Digital processing of speech signal and the voice recognition algorithm is very important for fast and accurate automatic scoring of the recognition technology. A voice is a signal of infinite information. The direct analysis and synthesis of a complex speech signal is due to the fact that the information is contained in the signal. Speech is the most natural way of communicating people. The task of speech recognition is to convert speech into a sequence of words using a computer program. This article presents an algorithm of extracting MFCC for speech recognition. The MFCC algorit

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Furui, Sadaoki. "Recent Advances in Voice Signal Processing. Application Technologies. Speaker Recognition." Journal of the Institute of Television Engineers of Japan 47, no. 12 (1993): 1600–1603. http://dx.doi.org/10.3169/itej1978.47.1600.

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Mahalakshmi, P. "A REVIEW ON VOICE ACTIVITY DETECTION AND MEL-FREQUENCY CEPSTRAL COEFFICIENTS FOR SPEAKER RECOGNITION (TREND ANALYSIS)." Asian Journal of Pharmaceutical and Clinical Research 9, no. 9 (2016): 360. http://dx.doi.org/10.22159/ajpcr.2016.v9s3.14352.

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ABSTRACTObjective: The objective of this review article is to give a complete review of various techniques that are used for speech recognition purposes overtwo decades.Methods: VAD-Voice Activity Detection, SAD-Speech Activity Detection techniques are discussed that are used to distinguish voiced from unvoicedsignals and MFCC- Mel Frequency Cepstral Coefficient technique is discussed which detects specific features.Results: The review results show that research in MFCC has been dominant in signal processing in comparison to VAD and other existing techniques.Conclusion: A comparison of differe

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Mühl, Constanze, and Patricia EG Bestelmeyer. "Assessing susceptibility to distraction along the vocal processing hierarchy." Quarterly Journal of Experimental Psychology 72, no. 7 (2018): 1657–66. http://dx.doi.org/10.1177/1747021818807183.

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Recent models of voice perception propose a hierarchy of steps leading from a more general, “low-level” acoustic analysis of the voice signal to a voice-specific, “higher-level” analysis. We aimed to engage two of these stages: first, a more general detection task in which voices had to be identified amid environmental sounds, and, second, a more voice-specific task requiring a same/different decision about unfamiliar speaker pairs (Bangor Voice Matching Test [BVMT]). We explored how vulnerable voice recognition is to interfering distractor voices, and whether performance on the aforementioned

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Djara, Tahirou, Abdoul Matine Ousmane, and Antoine Vianou. "Emotional State Recognition Using Facial Expression, Voice, and Physiological Signal." International Journal of Robotics Applications and Technologies 6, no. 1 (2018): 1–20. http://dx.doi.org/10.4018/ijrat.2018010101.

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Emotion recognition is an important aspect of affective computing, one of whose aims is the study and development of behavioral and emotional interaction between human and machine. In this context, another important point concerns acquisition devices and signal processing tools which lead to an estimation of the emotional state of the user. This article presents a survey about concepts around emotion, multimodality in recognition, physiological activities and emotional induction, methods and tools for acquisition and signal processing with a focus on processing algorithm and their degree of re

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P, Ramadevi, and . "A Novel User Interface for Text Dependent Human Voice Recognition System." International Journal of Engineering & Technology 7, no. 4.6 (2018): 285. http://dx.doi.org/10.14419/ijet.v7i4.6.20714.

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In an effort to provide a more efficient representation of the speech signal, the application of the wavelet analysis is considered. This research presents an effective and robust method for extracting features for speech processing. Here, we proposed a novel user interface for Text Dependent Human Voice Recognition (TD-HVR) system. The proposed HVR model utilizes decimated bi-orthogonal wavelet transform (DBT) approach to extract the low level features from the given input voice signal, then the noise elimination will be done by band pass filtering followed by normalization for better quality

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P, Ramadevi, and . "A Novel User Interface for Text Dependent Human Voice Recognition System." International Journal of Engineering & Technology 7, no. 4.6 (2018): 258. http://dx.doi.org/10.14419/ijet.v7i4.6.21193.

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In an effort to provide a more efficient representation of the speech signal, the application of the wavelet analysis is considered. This research presents an effective and robust method for extracting features for speech processing. Here, we proposed a novel user interface for Text Dependent Human Voice Recognition (TD-HVR) system. The proposed HVR model utilizes decimated bi-orthogonal wavelet transform (DBT) approach to extract the low level features from the given input voice signal, then the noise elimination will be done by band pass filtering followed by normalization for better quality

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Wei, Yan Ping, and Hai Liu Xiao. "Design of Voice Signal Visualization Acquisition System Based on Sound Card and MATLAB." Applied Mechanics and Materials 716-717 (December 2014): 1272–76. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.1272.

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With the development of computer technology and information technology, voice interaction has become a necessary means of human-computer interaction, and voice signal acquisition and processing is the precondition and foundation of human-computer interaction. This paper introduces the MATLAB visualization method into voice signal acquisition system, and uses MATLAB programming method to drive sound card directly, which realizes the identification and acquisition of voice signal and designs a new voice signal visualization acquisition system. In order to optimize the system, this paper introduc

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Sani, Dian Ahkam, and Muchammad Saifulloh. "Speech to Text Processing for Interactive Agent of Virtual Tour Navigation." International Journal of Artificial Intelligence & Robotics (IJAIR) 1, no. 1 (2019): 31. http://dx.doi.org/10.25139/ijair.v1i1.2030.

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The development of science and technology is one way to replace the method of human interaction with computers, one of which is to provide voice input. Conversion of sound into text form with the Backpropagation method can be understood and realized through feature extraction, including the use of Linear Predictive Coding (LPC). Linear Predictive Coding is one way to represent the signal in obtaining the features of each sound pattern. In brief, the way this speech recognition system worked was by inputting human voice through a microphone (analog signal) which then sampled with a sampling spe

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Watanabe, Takao. "Recent Advances in Voice Signal Processing. Fundamental Technologies. Continuous Speech Recognition." Journal of the Institute of Television Engineers of Japan 47, no. 12 (1993): 1583–87. http://dx.doi.org/10.3169/itej1978.47.1583.

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Yin, Shu Hua. "Design of the Auxiliary Speech Recognition System of Super-Short-Range Reconnaissance Radar." Applied Mechanics and Materials 556-562 (May 2014): 4830–34. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4830.

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To improve the usability and operability of the hybrid-identification reconnaissance radar for individual use, a voice identification System was designed. By using SPCE061A audio signal microprocessor as the core, a digital signal processing technology was used to obtain Doppler radar signals of audio segments by audio cable. Afterwards, the A/D acquisition was conducted to acquire digital signals, and then the data obtained were preprocessed and adaptively filtered to eliminate background noises. Moreover, segmented FFT transforming was used to identify the types of the signals. The overall d

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HimaBindu, Gottumukkala, Gondi Lakshmeeswari, Giddaluru Lalitha, and Pedalanka P. S. Subhashini. "Recognition Using DNN with Bacterial Foraging Optimization Using MFCC Coefficients." Journal Européen des Systèmes Automatisés 54, no. 2 (2021): 283–87. http://dx.doi.org/10.18280/jesa.540210.

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Speech is an important mode of communication for people. For a long time, researchers have been working hard to develop conversational machines which will communicate with speech technology. Voice recognition is a part of a science called signal processing. Speech recognition is becoming more successful for providing user authentication. The process of user recognition is becoming more popular now a days for providing security by authenticating the users. With the rising importance of automated information processing and telecommunications, the usefulness of recognizing an individual from the

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Mispagel, Karen M., and Michael Valente. "Effect of Multichannel Digital Signal Processing on Loudness Comfort, Sentence Recognition, and Sound Quality." Journal of the American Academy of Audiology 17, no. 10 (2006): 681–707. http://dx.doi.org/10.3766/jaaa.17.10.2.

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This study evaluated the effect of increasing the number of processing channels from 32- to 64-signal processing channels on subjects' loudness comfort and satisfaction, sentence recognition, and sound quality of his or her own voice. Ten experienced hearing aid users with mild-to-moderate sensorineural hearing loss wore behind-the-ear (BTE) hearing aids with Adaptive Dynamic Range Optimization (ADRO™) signal processing for a period of six weeks in the 32-channel and 64-channel conditions. Results revealed no significant differences in loudness comfort or satisfaction for the majority of sound

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Nair, Vani, Pooja Pillai, Anupama Subramanian, Sarah Khalife, and Dr Madhu Nashipudimath. "Voice Feature Extraction for Gender and Emotion Recognition." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 5 (2021): 17–22. http://dx.doi.org/10.17762/ijritcc.v9i5.5463.

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Voice recognition plays a key role in spoken communication that helps to identify the emotions of a person that reflects in the voice. Gender classification through speech is a widely used Human Computer Interaction (HCI) as it is not easy to identify gender by computer. This led to the development of a model for “Voice feature extraction for Emotion and Gender Recognition”. The speech signal consists of semantic information, speaker information (gender, age, emotional state), accompanied by noise. Females and males have different voice characteristics due to their acoustical and perceptual di

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Nashipudimath, Madhu M., Pooja Pillai, Anupama Subramanian, Vani Nair, and Sarah Khalife. "Voice Feature Extraction for Gender and Emotion Recognition." ITM Web of Conferences 40 (2021): 03008. http://dx.doi.org/10.1051/itmconf/20214003008.

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Voice recognition plays a key function in spoken communication that facilitates identifying the emotions of a person that reflects within the voice. Gender classification through speech is a popular Human Computer Interaction (HCI) method on account that determining gender through computer is hard. This led to the development of a model for "Voice feature extraction for Emotion and Gender Recognition". The speech signal consists of semantic information, speaker information (gender, age, emotional state), accompanied by noise. Females and males have specific vocal traits because of their acoust

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Mohd Hanifa, Rafizah, Khalid Isa, Shamsul Mohamad, et al. "Voiced and unvoiced separation in malay speech using zero crossing rate and energy." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 2 (2019): 775. http://dx.doi.org/10.11591/ijeecs.v16.i2.pp775-780.

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<p>This paper contributes to the literature on voice-recognition in the context of non-English language. Specifically, it aims to validate the techniques used to present the basic characteristics of speech, viz. voiced and unvoiced, that need to be evaluated when analysing speech signals. Zero Crossing Rate (ZCR) and Short Time Energy (STE) are used in this paper to perform signal pre-processing of continuous Malay speech to separate the voiced and unvoiced parts. The study is based on non-real time data which was developed from a collection of audio speeches. The signal is assessed usin

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Shi, Li Juan, Ping Feng, Jian Zhao, Li Rong Wang, and Na Che. "Study on Dual Mode Fusion Method of Video and Audio." Applied Mechanics and Materials 734 (February 2015): 412–15. http://dx.doi.org/10.4028/www.scientific.net/amm.734.412.

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In order to solve the hearing-impaired students in class only rely on sign language, amount of classroom information received less, This paper studies video and audio dual mode fusion algorithm combined with lip reading、speech recognition technology and information fusion technology.First ,speech feature extraction, processing of speech signal, the speech synchronization output text. At the same time, extraction of video features, voice and video signal fusion, Make voice information into visual information that the hearing-impaired students can receive. Make the students receive text messages

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Hekiert, Daniela, and Magdalena Igras-Cybulska. "Capturing emotions in voice: A comparative analysis of methodologies in psychology and digital signal processing." Roczniki Psychologiczne 22, no. 1 (2019): 15–34. http://dx.doi.org/10.18290/rpsych.2019.22.1-2.

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People use their voices to communicate not only verbally but also emotionally. This article presents theories and methodologies that concern emotional vocalizations at the intersection of psychology and digital signal processing. Specifically, it demonstrates the encoding (production) and decoding (recognition) of emotional sounds, including the review and comparison of strategies in database design, parameterization, and classification. Whereas psychology predominantly focuses on the subjective recognition of emotional vocalizations, digital signal processing relies on automated and thus more

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Xu, Yang, Zhe Zhang, and Zhi Yu Huang. "Vehicle Embedded Speech Recognition and Control System Research and Implementation." Applied Mechanics and Materials 494-495 (February 2014): 104–7. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.104.

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For the driver in the process of moving inconvenient to manually operated vehicle electronics, as well as the monopoly of foreign technology and other issues related , a framework based on DSP + MCU car speech recognition and control systems is designed. According to the embedded application environment, the corresponding recognition algorithm and the hardware architecture of DSP + MCU are chosen, in which DSP is mainly responsible for voice signal processing work, MCU is responsible for communicating with DSP and MCU to obtain recognition results after speech signal processing, as the final c

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Kang, Sang-Ick, and Sangmin Lee. "Improvement of Speech/Music Classification for 3GPP EVS Based on LSTM." Symmetry 10, no. 11 (2018): 605. http://dx.doi.org/10.3390/sym10110605.

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The competition of speech recognition technology related to smartphones is now getting into full swing with the widespread internet of thing (IoT) devices. For robust speech recognition, it is necessary to detect speech signals in various acoustic environments. Speech/music classification that facilitates optimized signal processing from classification results has been extensively adapted as an essential part of various electronics applications, such as multi-rate audio codecs, automatic speech recognition, and multimedia document indexing. In this paper, we propose a new technique to improve

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Czap, Laszlo, and Judit Pinter. "Noise Reduction in Voice Controlled Logistic Systems." Applied Mechanics and Materials 309 (February 2013): 260–67. http://dx.doi.org/10.4028/www.scientific.net/amm.309.260.

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The most comfortable way of human communication is speech, which is a possible channel of human-machine interface as well. Moreover, a voice driven system can be controlled with busy hands. Performance of a speech recognition system is highly decayed by presence of noise. Logistic systems typically work in noisy environment, so noise reduction is crucial in industrial speech processing systems. Traditional noise reduction procedures (e.g. Wiener and Kalman filters) are effective on stationary or Gaussian noise. The noise of a real workplace can be captured by an additional microphone: The voic

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de Abreu, Caio Cesar Enside, Marco Aparecido Queiroz Duarte, Bruno Rodrigues de Oliveira, Jozue Vieira Filho, and Francisco Villarreal. "Regression-Based Noise Modeling for Speech Signal Processing." Fluctuation and Noise Letters 20, no. 03 (2021): 2150022. http://dx.doi.org/10.1142/s021947752150022x.

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Speech processing systems are very important in different applications involving speech and voice quality such as automatic speech recognition, forensic phonetics and speech enhancement, among others. In most of them, the acoustic environmental noise is added to the original signal, decreasing the signal-to-noise ratio (SNR) and the speech quality by consequence. Therefore, estimating noise is one of the most important steps in speech processing whether to reduce it before processing or to design robust algorithms. In this paper, a new approach to estimate noise from speech signals is presente

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Xue, Lei, Zhi Zhang, Xiaoyang Zhang, and Yiwen Zhang. "Research and Implementation of Children’s Speech Signal Processing System." Open Biomedical Engineering Journal 9, no. 1 (2015): 188–93. http://dx.doi.org/10.2174/1874120701509010188.

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As people's living standard and the degree of mass culture have been constantly improved, many families are caring more about the healthy growth of early childhood. In this paper, based on the research of domestic and foreign experts and scholars: the guardians (such as parents) take appropriate intervention on children at the early stage can effectively promote children's language and cognitive ability development, and the intervention has obvious effect on the autistic spectrum disorders of children. This paper presents a system for analyzing children's speech signal, calculating the guardia

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Manoharan, Samuel, and Narain Ponraj. "Analysis of Complex Non-Linear Environment Exploration in Speech Recognition by Hybrid Learning Technique." December 2020 2, no. 4 (2021): 202–9. http://dx.doi.org/10.36548//jiip.2020.4.005.

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Recently, the application of voice-controlled interfaces plays a major role in many real-time environments such as a car, smart home and mobile phones. In signal processing, the accuracy of speech recognition remains a thought-provoking challenge. The filter designs assist speech recognition systems in terms of improving accuracy by parameter tuning. This task is some degree of form filter’s narrowed specifications which lead to complex nonlinear problems in speech recognition. This research aims to provide analysis on complex nonlinear environment and exploration with recent techniques in the

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Manoharan, Samuel, and Narain Ponraj. "Analysis of Complex Non-Linear Environment Exploration in Speech Recognition by Hybrid Learning Technique." December 2020 2, no. 4 (2021): 202–9. http://dx.doi.org/10.36548/jiip.2020.4.005.

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Recently, the application of voice-controlled interfaces plays a major role in many real-time environments such as a car, smart home and mobile phones. In signal processing, the accuracy of speech recognition remains a thought-provoking challenge. The filter designs assist speech recognition systems in terms of improving accuracy by parameter tuning. This task is some degree of form filter’s narrowed specifications which lead to complex nonlinear problems in speech recognition. This research aims to provide analysis on complex nonlinear environment and exploration with recent techniques in the

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Benítez-Guijarro, Callejas, Noguera, and Benghazi. "Coordination of Speech Recognition Devices in Intelligent Environments with Multiple Responsive Devices." Proceedings 31, no. 1 (2019): 54. http://dx.doi.org/10.3390/proceedings2019031054.

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Devices with oral interfaces are enabling new interesting interaction scenarios and ways of interaction in ambient intelligence settings. The use of several of such devices in the same environment opens up the possibility to compare the inputs gathered from each one of them and perform a more accurate recognition and processing of user speech. However, the combination of multiple devices presents coordination challenges, as the processing of one voice signal by different speech processing units may result in conflicting outputs and it is necessary to decide which is the most reliable source. T

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Ganesh, Venkateshwaran, and C. Sujatha. "Ingenious Traffic Control System with Green Signal Timings Using Image Processing." Advanced Science, Engineering and Medicine 12, no. 3 (2020): 337–41. http://dx.doi.org/10.1166/asem.2020.2502.

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In metropolis, traffic congestion affects the daily routine of passengers and in the long run there will be a declination in productivity if such situation is left unaddressed. If an Ambulance, unfortunately, stuck in the middle of congested road, any delay can endanger the life of the patient and, such cases require intelligent, powerful and reliable traffic control system. In this paper, the Infra-Red (IR) Sensors keep track of vehicle density across the lane. The micro-controller in turn, generates the control signals to alter the traffic accordingly. During each transition phase, the Voice

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Wheatley, Barbara, and Joseph Picone. "Voice across America: Toward robust speaker-independent speech recognition for telecommunications applications." Digital Signal Processing 1, no. 2 (1991): 45–63. http://dx.doi.org/10.1016/1051-2004(91)90095-3.

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Navakauskas, Dalius, and Šarūnas Paulikas. "Autonomous Robot in an Adverse Environment: Intelligent Control by Voice." Solid State Phenomena 113 (June 2006): 325–28. http://dx.doi.org/10.4028/www.scientific.net/ssp.113.325.

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In this paper we investigate the voice control of an autonomous robot in the presence of impulsive noise. We propose an original structure of the intelligent voice control system, present experimental investigation of separate modules and outline the performance of the system by the simulation example. Our approach differs from others in twofold: the noise detection is carried out by specialized artificial neural network; and the restoration of the missing speech signal is performed by using an intelligent multirate-processing scheme. The simplicity of neural network’s employment and unnecessa

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Gao, Mei Juan, and Zhi Xin Yang. "Research and Realization on the Voice Command Recognition System for Robot Control Based on ARM9." Applied Mechanics and Materials 44-47 (December 2010): 1422–26. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.1422.

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In this paper, based on the study of two speech recognition algorithms, two designs of speech recognition system are given to realize this isolated speech recognition mobile robot control system based on ARM9 processor. The speech recognition process includes pretreatment of speech signal, characteristic extrication, pattern matching and post-processing. Mel-Frequency cepstrum coefficients (MFCC) and linear prediction cepstrum coefficients (LPCC) are the two most common parameters. Through analysis and comparison the parameters, MFCC shows more noise immunity than LPCC, so MFCC is selected as

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Lutsenko, K., and K. Nikulin. "VOICE SPEAKER IDENTIFICATION AS ONE OF THE CURRENT BIOMETRIC METHODS OF IDENTIFICATION OF A PERSON." Theory and Practice of Forensic Science and Criminalistics 19, no. 1 (2020): 239–55. http://dx.doi.org/10.32353/khrife.1.2019.18.

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The article deals with the most widespread biometric identification systems of individuals, including voice recognition of the speaker on video and sound recordings. The urgency of the topic of identification of a person is due to the active informatization of modern society and the increase of flows of confidential information. The branches of the use of biometric technologies and their general characteristics are given. Here is an overview of the use of identification groups that characterize the voice. Also in the article the division of voice identification systems into the correspon

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Ma, Lina, and Yanjie Lei. "Optimization of Computer Aided English Pronunciation Teaching System Based on Speech Signal Processing Technology." Computer-Aided Design and Applications 18, S3 (2020): 129–40. http://dx.doi.org/10.14733/cadaps.2021.s3.129-140.

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After the development of speech signal processing technology has matured, various language learning tools have begun to emerge. The speech signal processing technology has many functions, such as standard tape reading, making audio aids, synthesizing speech, and performing speech evaluation. Therefore, the adoption of speech signal processing technology in English pronunciation teaching can meet different teaching needs. Voice signal processing technology can present teaching information in different forms, and promote multi-form communication between teachers and students, and between student

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ESPOSITO, ANNA, VOJTĚCH STEJSKAL, and ZDENĚK SMÉKAL. "COGNITIVE ROLE OF SPEECH PAUSES AND ALGORITHMIC CONSIDERATIONS FOR THEIR PROCESSING." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 05 (2008): 1073–88. http://dx.doi.org/10.1142/s0218001408006508.

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This study investigates pausing strategies, focusing the attention on empty speech pauses. A cross-modal analysis (video and audio) of spontaneous narratives produced by male and female children and adults showed that a remarkable amount of empty speech pauses was used to signal new concepts in the speech flow and to segment discourse units such as clauses and paragraphs. Based on these results, an adaptive mathematical model for pause distribution was suggested, that exploits, as pause features, the absence of signal and/or the changes of energy over different acoustic dimensions strongly rel

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Yang, Hai, Yunfei Xu, Houjun Huang, Ruohua Zhou, and Yonghong Yan. "Voice biometrics using linear Gaussian model." IET Biometrics 3, no. 1 (2014): 9–15. http://dx.doi.org/10.1049/iet-bmt.2013.0027.

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Ting, Liu, and Luo Xinwei. "An improved voice activity detection method based on spectral features and neural network." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 2 (2021): 4570–80. http://dx.doi.org/10.3397/in-2021-2747.

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The recognition accuracy of speech signal and noise signal is greatly affected under low signal-to-noise ratio. The neural network with parameters obtained from the training set can achieve good results in the existing data, but is poor for the samples with different the environmental noises. This method firstly extracts the features based on the physical characteristics of the speech signal, which have good robustness. It takes the 3-second data as samples, judges whether there is speech component in the data under low signal-to-noise ratios, and gives a decision tag for the data. If a reason

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Muhaxov, Harhenbek, Zhong Lou, Wang Li, Tolewbek Samet, and Aheyeh Harhenbek. "Experimental Research on Signal Recognition Algorithm of Wireless Sensor Language." International Journal of Online Engineering (iJOE) 12, no. 10 (2016): 38. http://dx.doi.org/10.3991/ijoe.v12i10.6203.

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<p style="margin: 0in 0in 10pt;"><span style="-ms-layout-grid-mode: line;"><span style="font-family: Times New Roman; font-size: small;">In the past several decades, much research has been carried out on the </span><a name="OLE_LINK9"></a><span style="font-family: Times New Roman; font-size: small;">wireless</span><span style="font-family: Times New Roman; font-size: small;"> sensor network which is widely used in the fields of national defense and national economy within China. The main function of the language sensor is to transfer the vo

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Badr, Ameer, and Alia Abdul-Hassan. "A Review on Voice-based Interface for Human-Robot Interaction." Iraqi Journal for Electrical and Electronic Engineering 16, no. 2 (2020): 1–12. http://dx.doi.org/10.37917/ijeee.16.2.10.

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With the recent developments of technology and the advances in artificial intelligence and machine learning techniques, it has become possible for the robot to understand and respond to voice as part of Human-Robot Interaction (HRI). The voice-based interface robot can recognize the speech information from humans so that it will be able to interact more naturally with its human counterpart in different environments. In this work, a review of the voice-based interface for HRI systems has been presented. The review focuses on voice-based perception in HRI systems from three facets, which are: fe

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Cheng, Xie Feng, Ye Wei Tao, and Zheng Jiang Huang. "Heart Sound Recognition - A Prospective Candidate for Biometric Identification." Advanced Materials Research 225-226 (April 2011): 433–36. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.433.

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Based on principles of human heart auscultation and the associated signal processing technology, we designed and manufactured "a double-header two-way voice auscultation detection device". The paper introduced a special human feature extraction method which is based on improved circle convolution (ICC) slicing algorithm combined with independent sub-band function (ISF). Follow we adopt a fire-new classification technology namely s1 and s2 model which is through two recognition steps to get different human’s heart sound features to assure validity, and then use similarity distance to carry out

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Dejonckere, P. H., A. Giordano, J. Schoentgen, S. Fraj, L. Bocchi, and C. Manfredi. "To what degree of voice perturbation are jitter measurements valid? A novel approach with synthesized vowels and visuo-perceptual pattern recognition." Biomedical Signal Processing and Control 7, no. 1 (2012): 37–42. http://dx.doi.org/10.1016/j.bspc.2011.05.002.

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Wu, Jian-Tong, Shinichi Tamura, Hiroshi Mitsumoto, Hideo Kawai, Kenji Kurosu, and Kozo Okazaki. "Neural network vowel-recognition jointly using voice features and mouth shape image." Pattern Recognition 24, no. 10 (1991): 921–27. http://dx.doi.org/10.1016/0031-3203(91)90089-n.

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Li, Haowei. "A Speaker Recognition System Based on Deep Learning." Journal of Electronic Research and Application 3, no. 6 (2019): 1–6. http://dx.doi.org/10.26689/jera.v3i6.1056.

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This paper lies in the field of digital signal processing. This is a speech recognition system that identifies the different speakers based on deep learning. The invention consists of the following steps: Firstly, we collect the voice data from different people. Secondly, the data having been selected is preprocessed by extracting their Mel Frequency Cepstral Coefficients (MFCC) and is divided into training set and test set randomly. Thirdly, we cut the training set into batches, and put them into the convolutional neural network which consists of convolutional layers, max pooling layers and f

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Sledevič, Tomyslav, and Liudas Stašionis. "FPGA-BASED IMPLEMENTATION OF LITHUANIAN ISOLATED WORD RECOGNITION ALGORITHM / LIETUVIŲ KALBOS PAVIENIŲ ŽODŽIŲ ATPAŽINIMO ALGORITMO ĮGYVENDINIMAS LAUKU PROGRAMUOJAMA LOGINE MATRICA." Mokslas - Lietuvos ateitis 5, no. 2 (2013): 101–4. http://dx.doi.org/10.3846/mla.2013.18.

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The paper describes the FPGA-based implementation of Lithuanian isolated word recognition algorithm. FPGA is selected for parallel process implementation using VHDL to ensure fast signal processing at low rate clock signal. Cepstrum analysis was applied to features extraction in voice. The dynamic time warping algorithm was used to compare the vectors of cepstrum coefficients. A library of 100 words features was created and stored in the internal FPGA BRAM memory. Experimental testing with speaker dependent records demonstrated the recognition rate of 94%. The recognition rate of 58% was achie

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Li, Jing Jiao, Dong An, Jiao Wang, and Chao Qun Rong. "Speech Endpoint Detection in Noisy Environment Based on the Ensemble Empirical Mode Decomposition." Advanced Engineering Forum 2-3 (December 2011): 135–39. http://dx.doi.org/10.4028/www.scientific.net/aef.2-3.135.

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Speech endpoint detection is one of the key problems in the practical application of speech recognition system. In this paper, speech signal contained chirp is decomposed into several intrinsic mode function (IMF) with the method of ensemble empirical mode decomposition (EEMD). At the same time, it eliminates the modal mix superposition phenomenon which usually comes out in processing speech signal with the algorithm of empirical mode decomposition (EMD). After that, selects IMFs contained major noise through the adaptive algorithm. Finally, the IMFs and speech signal contained chirp are input

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Wu, Jian Da, Pang Yi Liu, and Guan Long Hong. "Driver Voice Identification System Using Auto-Correlation Function and Average Magnitude Difference Function." Applied Mechanics and Materials 490-491 (January 2014): 1287–92. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1287.

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This study presents a driver identification system using voice analysis for a vehicle security system. The structure of the proposed system has three parts. The first procedure is speech pre-processing, the second is feature extraction of sound signals, and the third is classification of driver voice. Initially, a database of sound signals for several drivers was established. The volume and zero-crossing rate (ZCR) of sound are used to detect the voice end-point in order to reduce data computation. Then the Auto-correlation Function (ACF) and Average Magnitude Difference Function (AMDF) method

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Laptev, O., V. Sobchuk, and V. Savchenko. "A METHOD OF INCREASING THE IMMUNITY OF A SYSTEM FOR DETECTING, RECOGNIZING AND LOCALIZING DIGITAL SIGNALS IN THE INFORMATION SYSTEMS." Collection of scientific works of the Military Institute of Kyiv National Taras Shevchenko University, no. 66 (2019): 90–104. http://dx.doi.org/10.17721/2519-481x/2020/66-09.

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In the process of detection, recognition, and localization of the single means of silent retrieval of information in information systems, the urgent issue is the increase of noise immunity. The article explores the features of using low-pass filters with a quadratic and linear response dependence on the input signal. It is shown that the principle of operation of the filters is that the summation process is performed. In this case, the useful signal is summed coherently, and the interference signal is incoherent, that is, the useful signal increases, and the interference signal decreases. When

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Shackleton, Trevor M., Ray Meddis, and Michael J. Hewitt. "The Role of Binaural and Fundamental Frequency Difference cues in the Identification of Concurrently Presented Vowels." Quarterly Journal of Experimental Psychology Section A 47, no. 3 (1994): 545–63. http://dx.doi.org/10.1080/14640749408401127.

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The relative importance of voice pitch and interaural difference cues in facilitating the recognition of both of two concurrently presented synthetic vowels was measured. The interaural difference cues used were an interaural time difference (400 μsec ITD), two magnitudes of interaural level difference (15 dB and infinite ILD), and a combination of ITD and ILD (400 μsec plus 15 dB). The results are analysed separately for those cases where both vowels are identical and those where they are different. When the two vowels are different, a voice pitch difference of one semitone is found to improv

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Beiderman, Yevgeny, Yaniv Azani, Yoni Cohen, et al. "Spatial Processing for Improved Quality Recognition of Optically Recorded Voice Signals and Illumination Varied Scenery." Recent Patents on Signal Processing 1, no. 2 (2011): 91–100. http://dx.doi.org/10.2174/1877612411101020091.

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Beiderman, Yevgeny, Yaniv Azani, Yoni Cohen, et al. "Spatial Processing for Improved Quality Recognition of Optically Recorded Voice Signals and Illumination Varied Scenery." Recent Patents on Signal Processinge 1, no. 2 (2011): 91–100. http://dx.doi.org/10.2174/2210686311101020091.

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