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Relevant bibliographies by topics / Signal processing; Voice recognition

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Academic literature on the topic 'Signal processing; Voice recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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

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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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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Dissertations / Theses on the topic "Signal processing; Voice recognition"

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Nayfeh, Taysir H. "Multi-signal processing for voice recognition in noisy environments." Thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-10222009-125021/.

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Fredrickson, Steven Eric. "Neural networks for speaker identification." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294364.

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Little, M. A. "Biomechanically informed nonlinear speech signal processing." Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:6f5b84fb-ab0b-42e1-9ac2-5f6acc9c5b80.

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Linear digital signal processing based around linear, time-invariant systems theory finds substantial application in speech processing. The linear acoustic source-filter theory of speech production provides ready biomechanical justification for using linear techniques. Nonetheless, biomechanical studies surveyed in this thesis display significant nonlinearity and non-Gaussinity, casting doubt on the linear model of speech production. In order therefore to test the appropriateness of linear systems assumptions for speech production, surrogate data techniques can be used. This study uncovers sys

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Regnier, Lise. "Localization, Characterization and Recognition of Singing Voices." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2012. http://tel.archives-ouvertes.fr/tel-00687475.

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This dissertation is concerned with the problem of describing the singing voice within the audio signal of a song. This work is motivated by the fact that the lead vocal is the element that attracts the attention of most listeners. For this reason it is common for music listeners to organize and browse music collections using information related to the singing voice such as the singer name. Our research concentrates on the three major problems of music information retrieval: the localization of the source to be described (i.e. the recognition of the elements corresponding to the singing voice

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Adami, Andre Gustavo. "Sistema de reconhecimento de locutor utilizando redes neurais artificiais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 1997. http://hdl.handle.net/10183/18277.

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Este trabalho envolve o emprego de recentes tecnologias ligadas a promissora área de Inteligência Computacional e a tradicional área de Processamento de Sinais Digitais. Tem por objetivo o desenvolvimento de uma aplicação especifica na área de Processamento de Voz: o reconhecimento de locutor. Inúmeras aplicações, ligadas principalmente a segurança e controle, são possíveis a partir do domínio da tecnologia de reconhecimento de locutor, tanto no que diz respeito a identificação quanto a verificação de diferentes locutores. O processo de reconhecimento de locutor pode ser dividido em duas grand

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Stolfi, Rumiko Oishi. "Sintese e reconhecimento da fala humana." [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276267.

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Orientadores: Fabio Violaro, Anamaria Gomide<br>Dissertação (mestrado profissional) - Universidade Estadual de Campinas, Instituto de Computação<br>Made available in DSpace on 2018-08-07T21:57:26Z (GMT). No. of bitstreams: 1 Stolfi_RumikoOishi_M.pdf: 1514197 bytes, checksum: e93f45916d359641c73b31b00952a914 (MD5) Previous issue date: 2006<br>Resumo: O objetivo deste trabalho é apresentar uma revisão dos principais conceitos e métodos envolvidos na síntese, processamento e reconhecimento da fala humana por computador.Estas tecnologias têm inúmeras aplicações, que têm aumentado substancialment

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Clotworthy, Christopher John. "A study of automated voice recognition." Thesis, Queen's University Belfast, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.356909.

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Wells, Ian. "Digital signal processing architectures for speech recognition." Thesis, University of the West of England, Bristol, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294705.

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Aggoun, Amar. "DPCM video signal/image processing." Thesis, University of Nottingham, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335792.

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Morris, Robert W. "Enhancement and recognition of whispered speech." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04082004-180338/unrestricted/morris%5frobert%5fw%5f200312%5fphd.pdf.

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Books on the topic "Signal processing; Voice recognition"

1

Juang, Jer-Nan. Signal prediction with input identification. National Aeronautics and Space Administration, Langley Research Center, 1999.

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Robert, Rodman, ed. Voice recognition. Artech House, 1997.

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P, Banks Stephen. Signal processing, image processing, and pattern recognition. Prentice Hall, 1990.

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Ślęzak, Dominik, Sankar K. Pal, Byeong-Ho Kang, Junzhong Gu, Hideo Kuroda, and Tai-hoon Kim, eds. Signal Processing, Image Processing and Pattern Recognition. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10546-3.

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Kim, Tai-hoon, Hojjat Adeli, Carlos Ramos, and Byeong-Ho Kang, eds. Signal Processing, Image Processing and Pattern Recognition. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27183-0.

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VoIP voice and fax signal processing. Wiley, 2008.

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Goldman, Thomas F. Voice Xpress: Basic skills in voice recognition. Prentice Hall, 2001.

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Kutza, Patricia. Voice recognition: Technologies, markets, opportunities. Business Communications Co., 2002.

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Thampi, Sabu M., Oge Marques, Sri Krishnan, Kuan-Ching Li, Domenico Ciuonzo, and Maheshkumar H. Kolekar, eds. Advances in Signal Processing and Intelligent Recognition Systems. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5758-9.

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Thampi, Sabu M., Alexander Gelbukh, and Jayanta Mukhopadhyay, eds. Advances in Signal Processing and Intelligent Recognition Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04960-1.

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Book chapters on the topic "Signal processing; Voice recognition"

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Osowska, Aleksandra, and Stanislaw Osowski. "Voice Command Recognition Using Statistical Signal Processing and SVM." In Advances in Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20521-8_6.

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Rabiner, Lawrence R. "Speech Recognition Based on Pattern Recognition Approaches." In Signal Processing. Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4684-7095-6_19.

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Mathias, Samuel Robert, and Katharina von Kriegstein. "Voice Processing and Voice-Identity Recognition." In Timbre: Acoustics, Perception, and Cognition. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14832-4_7.

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Omologo, Maurizio, Marco Matassoni, and Piergiorgio Svaizer. "Speech Recognition with Microphone Arrays." In Digital Signal Processing. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04619-7_15.

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Owens, F. J. "Automatic Speech Recognition." In Signal Processing of Speech. Macmillan Education UK, 1993. http://dx.doi.org/10.1007/978-1-349-22599-6_7.

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Gorin, A. L., D. B. Roe, and A. G. Greenberg. "On the Complexity of Pattern Recognition Algorithms on a Tree-Structured Parallel Computer." In Signal Processing. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4684-6393-4_8.

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Haykin, Simon. "Modern Signal Processing." In Signal Processing and Pattern Recognition in Nondestructive Evaluation of Materials. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83422-6_3.

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Maher, Robert C. "Application Example 2: Cockpit Voice Recorders." In Modern Acoustics and Signal Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99453-6_10.

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Favre, Sarah. "Turns Analysis for Automatic Role Recognition." In Mobile Social Signal Processing. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54325-8_2.

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Derawi, Mohammad, Patrick Bours, and Ray Chen. "Biometric Acoustic Ear Recognition." In Signal Processing for Security Technologies. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47301-7_4.

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Conference papers on the topic "Signal processing; Voice recognition"

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Berdibaeva, Gulmira K., Oleg N. Bodin, Valery V. Kozlov, Dmitry I. Nefed'ev, Kasymbek A. Ozhikenov, and Yaroslav A. Pizhonkov. "Pre-processing voice signals for voice recognition systems." In 2017 18th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM). IEEE, 2017. http://dx.doi.org/10.1109/edm.2017.7981748.

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Parlak, C., and B. Diri. "Emotion recognition from the human voice." In 2013 21st Signal Processing and Communications Applications Conference (SIU). IEEE, 2013. http://dx.doi.org/10.1109/siu.2013.6531196.

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Greeley, H. P., E. Friets, J. P. Wilson, S. Raghavan, J. Picone, and J. Berg. "Detecting Fatigue From Voice Using Speech Recognition." In 2006 IEEE International Symposium on Signal Processing and Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/isspit.2006.270865.

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Baygin, Mehmet, and Mehmet Karakose. "Real time voice recognition based smart home application." In 2012 20th Signal Processing and Communications Applications Conference (SIU). IEEE, 2012. http://dx.doi.org/10.1109/siu.2012.6204694.

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Surendran, Dinoj, and Gina-Anne Levow. "Can voice quality improve mandarin tone recognition?" In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518575.

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Liang, Huixin, Xiaodan Lin, Qiong Zhang, and Xiangui Kang. "Recognition of spoofed voice using convolutional neural networks." In 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2017. http://dx.doi.org/10.1109/globalsip.2017.8308651.

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Jiang, Dan-ning, Michael Picheny, and Yong Qin. "Voice-Melody Transcription Under a Speech Recognition Framework." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366988.

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Tezer, Huseyin Kursat, and M. Yagimli. "Navigation autopilot with real time voice command recognition system." In 2013 21st Signal Processing and Communications Applications Conference (SIU). IEEE, 2013. http://dx.doi.org/10.1109/siu.2013.6531376.

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Jacob, Agnes. "Speech emotion recognition based on minimal voice quality features." In 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754275.

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Acosta Bedoya, William, and Leonardo Duque Munoz. "Methodology for voice commands recognition using stochastic classifiers." In 2012 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA). IEEE, 2012. http://dx.doi.org/10.1109/stsiva.2012.6340559.

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Reports on the topic "Signal processing; Voice recognition"

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Liu, Fu-Hua, Pedro J. Moreno, Richard M. Stern, and Alejandro Acero. Signal Processing for Robust Speech Recognition. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada457798.

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Shamma, Shihab A., and P. S. Krishnaprasad. Signal Processing and Recognition in Adaptive Neural Networks. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada250505.

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Sherlock, Barry G. Wavelet-Based Signal and Image Processing for Target Recognition. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada409223.

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Gribok, Andrei V. Performance of Advanced Signal Processing and Pattern Recognition Algorithms Using Raw Data from Ultrasonic Guided Waves and Fiber Optics Transducers. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1495185.

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