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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"

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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 to process the speech signals to extract the needed content while rejecting others (such as background noise). This paper presents a speech purification system for robots to improve the signal-to-noise ratio of reception and an algorithm with a multidirection calibration beamformer.

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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 algorithm reduces the processing power by 53% compared to the conventional algorithm. Automatic speech recognition using Matlab.

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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 different speaker recognition techniques that were used previously were discussed and those in current research werealso discussed and a clear idea of the better technique was identified through the review of multiple literature for over two decades.Keywords: Cepstral analysis, Mel-frequency cepstral coefficients, signal processing, speaker recognition, voice activity detection.

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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 tasks could predict resistance against such interference. In addition, we manipulated the similarity of distractor voices to explore the impact of distractor similarity on recognition accuracy. We found moderate correlations between voice detection ability and resistance to distraction ( r = .44), and BVMT and resistance to distraction ( r = .57). A hierarchical regression revealed both tasks as significant predictors of the ability to tolerate distractors ( R2 = .36). The first stage of the regression (BVMT as sole predictor) already explained 32% of the variance. Descriptively, the “higher-level” BVMT was a better predictor (β = .47) than the more general detection task (β = .25), although further analysis revealed no significant difference between both beta weights. Furthermore, distractor similarity did not affect performance on the distractor task. Overall, our findings suggest the possibility to target specific stages of the voice perception process. This could help explore different stages of voice perception and their contributions to specific auditory abilities, possibly also in forensic and clinical settings.

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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 reliability.

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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 of a voice signal and finally the formants of a train and test voices will be calculated by using the Additive Prognostication (AP) algorithm. Simulation results have been compared with the existing HVR schemes, and shown that the proposed user interface system has performed superior to the conventional HVR systems with an accuracy rate of approximately 99 %.

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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 of a voice signal and finally the formants of a train and test voices will be calculated by using the Additive Prognostication (AP) algorithm. Simulation results have been compared with the existing HVR schemes, and shown that the proposed user interface system has performed superior to the conventional HVR systems with an accuracy rate of approximately 99 %.

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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 introduces the variance analysis algorithm into the design of visualization system, which realizes the optimization of voice signal recognition model with different level parameters. At the end this paper does numerical simulation on the speech signal acquisition system; through signal acquisition 2D and 3D visualization voice signals are obtained. It extracts single signal characteristics, which provides a theoretical reference for the design of signal acquisition system.

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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 systematic flaws in the design and use of exiting surrogate data techniques, and, by making novel improvements, develops a more reliable technique. Collating the largest set of speech signals to-date compatible with this new technique, this study next demonstrates that the linear assumptions are not appropriate for all speech signals. Detailed analysis shows that while vowel production from healthy subjects cannot be explained within the linear assumptions, consonants can. Linear assumptions also fail for most vowel production by pathological subjects with voice disorders. Combining this new empirical evidence with information from biomechanical studies concludes that the most parsimonious model for speech production, explaining all these findings in one unified set of mathematical assumptions, is a stochastic nonlinear, non-Gaussian model, which subsumes both Gaussian linear and deterministic nonlinear models. As a case study, to demonstrate the engineering value of nonlinear signal processing techniques based upon the proposed biomechanically-informed, unified model, the study investigates the biomedical engineering application of disordered voice measurement. A new state space recurrence measure is devised and combined with an existing measure of the fractal scaling properties of stochastic signals. Using a simple pattern classifier these two measures outperform all combinations of linear methods for the detection of voice disorders on a large database of pathological and healthy vowels, making explicit the effectiveness of such biomechanically-informed, nonlinear signal processing techniques.

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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 in the signal of a mixture of instruments), the search of pertinent features to describe the singing voice, and finally the development of pattern recognition methods based on these features to identify the singer. For this purpose we propose a set of novel features computed on the temporal variations of the fundamental frequency of the sung melody. These features, which aim to describe the vibrato and the portamento, are obtained with the aid of a dedicated model. In practice, these features are computed on the time-varying frequency of partials obtained using the sinusoidal model. In the first experiment we show that partials corresponding to the singing voice can be accurately differentiated from the partials produced by other instruments using decisions based on the parameters of the vibrato and the portamento. Once the partials emitted by the singer are identified, the segments of the song containing singing can be directly localized. To improve the recognition of the partials emitted by the singer we propose to group partials that are related harmonically. Partials are clustered according to their degree of similarity. This similarity is computed using a set of CASA cues including their temporal frequency variations (i.e. the vibrato and the portamento). The clusters of harmonically related partials corresponding to the singing voice are identified using the vocal vibrato and the portamento parameters. Groups of vocal partials can then be re-synthesized to isolate the voice. The result of the partial grouping can also be used to transcribe the sung melody. We then propose to go further with these features and study if the vibrato and portamento characteristics can be considered as a part of the singers' signature. Previous works on singer identification describe audio signals using features extracted on the short-term amplitude spectrum. The latter features aim to characterize the timbre of the sound, which, in the case of singing, is related to the vocal tract of the singer. The features we develop in this document capture long-term information related to the intonation of the singer, which is relevant to the style and the technique of the singer. We propose a method to combine these two complementary descriptions of the singing voice to increase the recognition rate of singer identification. In addition we evaluate the robustness of each type of feature against a set of variations. We show the singing voice is a highly variable instrument. To obtain a representative model of a singer's voice it is thus necessary to build models using a large set of examples covering the full tessitura of a singer. In addition, we show that features extracted directly from the partials are more robust to the presence of an instrumental accompaniment than features derived from the amplitude spectrum.

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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 grandes fases: extração das características básicas do sinal de voz e classificação. Na fase de extração, procurou-se aplicar os mais recentes avanços na área de Processamento Digital de Sinais ao problema proposto. Neste contexto, foram utilizadas a frequência fundamental e as frequências formantes como parâmetros que identificam o locutor. O primeiro foi obtido através do use da autocorrelação e o segundo foi obtido através da transformada de Fourier. Estes parâmetros foram extraídos na porção da fala onde o trato vocal apresenta uma coarticulação entre dois sons vocálicos. Esta abordagem visa extrair as características desta mudança do aparato vocal. Existem dois tipos de reconhecimento de locutor: identificação (busca-se reconhecer o locutor em uma população) e verificação (busca-se verificar se a identidade alegada é verdadeira). O processo de reconhecimento de locutor é dividido em duas grandes fases: extração das características (envolve aquisição, pré-processamento e extração dos parâmetros característicos do sinal) e classificação (envolve a classificação do sinal amostrado na identificação/verificação do locutor ou não). São apresentadas diversas técnicas para representação do sinal, como analise espectral, medidas de energia, autocorrelação, LPC (Linear Predictive Coding), entre outras. Também são abordadas técnicas para extração de características do sinal, como a frequência fundamental e as frequências formantes. Na fase de classificação, pode-se utilizar diversos métodos convencionais: Cadeias de Markov, Distância Euclidiana, entre outros. Além destes, existem as Redes Neurais Artificiais (RNAs) que são consideradas poderosos classificadores. As RNAs já vêm sendo utilizadas em problemas que envolvem classificações de sinais de voz. Neste trabalho serão estudados os modelos mais utilizados para o problema de reconhecimento de locutor. Assim, o tema principal da Dissertação de Mestrado deste autor é a implementação de um sistema de reconhecimento de locutor utilizando Redes Neurais Artificiais para classificação do locutor. Neste trabalho tamb6m é apresentada uma abordagem para a implementação de um sistema de reconhecimento de locutor utilizando as técnicas convencionais para o processo de classificação do locutor. As técnicas utilizadas são Dynamic Time Warping (DTW) e Vector Quantization (VQ).<br>This work deals with the application of recent technologies related to the promising research domain of Intelligent Computing (IC) and to the traditional Digital Signal Processing area. This work aims to apply both technologies in a Voice Processing specific application which is the speaker recognition task. Many security control applications can be supported by speaker recognition technology, both in identification and verification of different speakers. The speaker recognition process can be divided into two main phases: basic characteristics extraction from the voice signal and classification. In the extraction phase, one proposed goal was the application of recent advances in DSP theory to the problem approached in this work. In this context, the fundamental frequency and the formant frequencies were employed as parameters to identify the speaker. The first one was obtained through the use of autocorrelation and the second ones were obtained through Fourier transform. These parameters were extracted from the portion of speech where the vocal tract presents a coarticulation between two voiced sounds. This approach is used to extract the characteristics of this apparatus vocal changing. In this work, the Multi-Layer Perceptron (MLP) ANN architecture was investigated in conjunction with the backpropagation learning algorithm. In this sense, some main characteristics extracted from the signal (voice) were used as input parameters to the ANN used. The output of MLP, trained previously with the speakers features, returns the authenticity of that signal. Tests were performed with 10 different male speakers, whose age were in the range from 18 to 24 years. The results are very promising. In this work it is also presented an approach to implement a speaker recognition system by applying conventional methods to the speaker classification process. The methods used are Dynamic Time Warping (DTW) and Vector Quantization (VQ).

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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 substancialmente nos últimos anos com a popularização de equipamentos de comunicação portáteis (celulares, laptops, palmtops) e a universalização da Internet. A primeira parte deste trabalho é uma revisão dos conceitos básicos de processamento de sinais, incluindo transformada de Fourier, espectro de potência e espectrograma, filtros, digitalização de sinais e o teorema de Nyquist. A segunda parte descreve as principais características da fala humana, os mecanismos envolvidos em sua produção e percepção, e o conceito de fone (unidade lingüística de som). Nessa parte também descrevemos brevemente as principais técnicas para a conversão ortográfica-fonética, para a síntese de fala a partir da descrição fonética, e para o reconhecimento da fala natural. A terceira parte descreve um projeto prático que desenvolvemos para consolidar os conhecimentos adquiridos neste mestrado: um programa que gera canções populares japonesas a partir de uma descrição textual da letra de música, usando método de síntese concatenativa. No final do trabalho listamos também alguns softwares disponíveis (livres e comerciais) para síntese e reconhecimento da fala<br>Abstract: The goal of this dissertation is to review the main concepts relating to the synthesis, processing, and recognition of human speech by computer. These technologies have many applications, which have increased substantially in recent years after the spread of portable communication equipment (mobile phones, laptops, palmtops) and the universal access to the Internet. The first part of this work is a revision of fundamental concepts of signal processing, including the Fourier transform, power spectrum and spectrogram, filters, signal digitalization, and Nyquist's theorem. The second part describes the main characteristics of human speech, the mechanisms involved in its production and perception, and the concept of phone (linguistic unit of sound). In this part we also briefly describe the main techniques used for orthographic-phonetic transcription, for speech synthesis from a phonetic description, and for the recognition of natural speech. The third part describes a practical project we developed to consolidate the knowledge acquired in our Masters studies: a program that generates Japanese popular songs from a textual description of the lyrics and music, using the concatenative synthesis method. At the end of this dissertation, we list some available software products (free and commercial) for speech synthesis and speech recognition<br>Mestrado<br>Engenharia de Computação<br>Mestre em Ciência da Computação

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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"

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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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