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Relevant bibliographies by topics / Speech pattern recognition / Dissertations / Theses

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Dissertations / Theses on the topic 'Speech pattern recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Milner, Benjamin Peter. "Speech recognition in adverse environments." Thesis, University of East Anglia, 1994. https://ueaeprints.uea.ac.uk/2907/.

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2

Long, Christopher J. "Wavelet methods in speech recognition." Thesis, Loughborough University, 1999. https://dspace.lboro.ac.uk/2134/14108.

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In this thesis, novel wavelet techniques are developed to improve parametrization of speech signals prior to classification. It is shown that non-linear operations carried out in the wavelet domain improve the performance of a speech classifier and consistently outperform classical Fourier methods. This is because of the localised nature of the wavelet, which captures correspondingly well-localised time-frequency features within the speech signal. Furthermore, by taking advantage of the approximation ability of wavelets, efficient representation of the non-stationarity inherent in speech can b

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3

Stewart, Darryl William. "Syllable based continuous speech recognition." Thesis, Queen's University Belfast, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325993.

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4

Luettin, Juergen. "Visual speech and speaker recognition." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264432.

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5

Allerhand, M. H. "A knowledge-based approach to speech pattern recognition." Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377200.

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6

Alexi, Ramsin. "A sub-neural network ensemble for speech recognition." Thesis, University of Sussex, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298656.

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7

Baothman, Fatmah bint Abdul Rahman. "Phonology-based automatic speech recognition for Arabic." Thesis, University of Huddersfield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273720.

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8

Holmes, Wendy Jane. "Modelling segmental variability for automatic speech recognition." Thesis, University College London (University of London), 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267859.

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9

Prager, Richard William. "Parallel processing networks for automatic speech recognition." Thesis, University of Cambridge, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238443.

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10

Alphonso, Issac John. "Network training for continuous speech recognition." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-10252003-105104.

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11

Mwangi, Elijah. "Speaker independent isolated word recognition." Thesis, Loughborough University, 1987. https://dspace.lboro.ac.uk/2134/15425.

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The work presented in this thesis concerns the recognition of isolated words using a pattern matching approach. In such a system, an unknown speech utterance, which is to be identified, is transformed into a pattern of characteristic features. These features are then compared with a set of pre-stored reference patterns that were generated from the vocabulary words. The unknown word is identified as that vocabulary word for which the reference pattern gives the best match. One of the major difficul ties in the pattern comparison process is that speech patterns, obtained from the same word, exhi

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12

Tran, Dat Tat, and n/a. "Fuzzy approaches to speech and peaker recognition." University of Canberra. Management & Technology, 2000. http://erl.canberra.edu.au./public/adt-AUC20061109.151916.

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Stastical pattern recognition is the most successful approach to automatic speech and speaker recognition (ASASR). Of all the statistical pattern recognition techniques, the hidden Markov model (HMM) is the most important. The Gaussian mixture model (GMM) and vector quantisation (VQ) are also effective techniques, especially for speaker recognition and in conjunction with HMMs. for speech recognition. However, the performance of these techniques degrades rapidly in the context of insufficient training data and in the presence of noise or distortion. Fuzzy approaches with their adjustable param

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13

Harte, Naomi Antonia. "Segmental phonetic features and models for speech recognition." Thesis, Queen's University Belfast, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287466.

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14

Khurshid, Azar. "Pitch estimation for noisy speech." Thesis, University of Plymouth, 2002. http://hdl.handle.net/10026.1/1692.

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In this dissertation a biologically plausible system of pitch estimation is proposed. The system is designed from the bottom up to be robust to challenging noise conditions. This robustness to the presence of noise in the signal is achieved by developing a new representation of the speech signal, based on the operation of damped harmonic oscillators, and temporal mode analysis of their output. This resulting representation is shown to possess qualities which are not degraded in presence of noise. A harmonic grouping based system is used to estimate the pitch frequency. A detailed statistical a

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15

Ma, Chengyuan. "A detection-based pattern recognition framework and its applications." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33889.

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The objective of this dissertation is to present a detection-based pattern recognition framework and demonstrate its applications in automatic speech recognition and broadcast news video story segmentation. Inspired by the studies of modern cognitive psychology and real-world pattern recognition systems, a detection-based pattern recognition framework is proposed to provide an alternative solution for some complicated pattern recognition problems. The primitive features are first detected and the task-specific knowledge hierarchy is constructed level by level; then a variety of heterogeneou

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16

Peng, Yong Kian. "Speech coding based on a pitch synchronous pattern recognition approach." Thesis, University of Ulster, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245804.

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17

Rao, Hrishikesh. "Paralinguistic event detection in children's speech." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54332.

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Paralinguistic events are useful indicators of the affective state of a speaker. These cues, in children's speech, are used to form social bonds with their caregivers. They have also been found to be useful in the very early detection of developmental disorders such as autism spectrum disorder (ASD) in children's speech. Prior work on children's speech has focused on the use of a limited number of subjects which don't have sufficient diversity in the type of vocalizations that are produced. Also, the features that are necessary to understand the production of paralinguistic events is not fully

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18

Josifovski, Ljubomir. "Robust automatic speech recognition with missing and unreliable data." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275021.

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19

Chong, Michael Wai Hing. "Subword units and parallel processing for automatic speech recognition." Thesis, University of Cambridge, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335663.

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20

Hewett, Andrew John. "Training and speaker adaptation in template-based speech recognition." Thesis, University of Cambridge, 1989. https://www.repository.cam.ac.uk/handle/1810/250961.

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21

Chen, Xin. "Ensemble methods in large vocabulary continuous speech recognition." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5797.

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Thesis (M.S.)--University of Missouri-Columbia, 2008.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 28, 2008) Vita. Includes bibliographical references.

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22

Wang, Xuechuan, and n/a. "Feature Extraction and Dimensionality Reduction in Pattern Recognition and Their Application in Speech Recognition." Griffith University. School of Microelectronic Engineering, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030619.162803.

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Conventional pattern recognition systems have two components: feature analysis and pattern classification. Feature analysis is achieved in two steps: parameter extraction step and feature extraction step. In the parameter extraction step, information relevant for pattern classification is extracted from the input data in the form of parameter vector. In the feature extraction step, the parameter vector is transformed to a feature vector. Feature extraction can be conducted independently or jointly with either parameter extraction or classification. Linear Discriminant Analysis (LDA) and Princi

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23

Lee, Gareth E. "Multi-modal prediction and modelling using artificial neural networks." Thesis, University of East Anglia, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293823.

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24

Chan, Dominic Sai Fan. "Speech production modelling based on glottal inverse filtering." Thesis, Imperial College London, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307161.

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25

Kounoudes, Anastasis. "Epoch estmimation for closed-phase analysis of speech." Thesis, Imperial College London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248213.

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26

Scott, Simon David. "A data-driven approach to visual speech synthesis." Thesis, University of Bath, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307116.

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27

Devaney, Jason Wayne. "A study of articulatory gestures for speech synthesis." Thesis, University of Liverpool, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284254.

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28

Savvides, Vasos E. "Perceptual models in speech quality assessment and coding." Thesis, Loughborough University, 1988. https://dspace.lboro.ac.uk/2134/36273.

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The ever-increasing demand for good communications/toll quality speech has created a renewed interest into the perceptual impact of rate compression. Two general areas are investigated in this work, namely speech quality assessment and speech coding. In the field of speech quality assessment, a model is developed which simulates the processing stages of the peripheral auditory system. At the output of the model a "running" auditory spectrum is obtained. This represents the auditory (spectral) equivalent of any acoustic sound such as speech. Auditory spectra from coded speech segments serve as

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29

Lo, Ka-Yiu. "Pitch synchronous speech coding at very low bit rates." Thesis, University of Liverpool, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321128.

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30

Farsi, Hassan. "Advanced pre-and-post processing techniques for speech coding." Thesis, University of Surrey, 2003. http://epubs.surrey.ac.uk/844491/.

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Advances in digital technology in the last decade have motivated the development of very efficient and high quality speech compression algorithms. While in the early low bit rate coding systems, the main target was the production of intelligible speech at low bit rates, expansion of new applications such as mobile satellite systems increased the demand for reducing the transmission bandwidth and achieving higher speech quality. This resulted in the development of efficient parametric models for speech production system. These models were the basis of powerful speech compression algorithms such

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31

Sidorova, Julia. "Optimization techniques for speech emotion recognition." Doctoral thesis, Universitat Pompeu Fabra, 2009. http://hdl.handle.net/10803/7575.

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Hay tres aspectos innovadores. Primero, un algoritmo novedoso para calcular el contenido emocional de un enunciado, con un diseño mixto que emplea aprendizaje estadístico e información sintáctica. Segundo, una extensión para selección de rasgos que permite adaptar los pesos y así aumentar la flexibilidad del sistema. Tercero, una propuesta para incorporar rasgos de alto nivel al sistema. Dichos rasgos, combinados con los rasgos de bajo nivel, permiten mejorar el rendimiento del sistema.<br>The first contribution of this thesis is a speech emotion recognition system called the ESEDA capable of

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32

Nag, Ronjan. "Speech and speaker recognition using hidden Markov models and vector quantisation." Thesis, University of Cambridge, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383109.

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33

Hanna, Philip James. "Improving speech recognition through statistical modelling of context and temporal dependency." Thesis, Queen's University Belfast, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287627.

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34

Hu, Rusheng. "Statistical optimization of acoustic models for large vocabulary speech recognition." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4329.

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Thesis (Ph. D.) University of Missouri-Columbia, 2006.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 2, 2007) Includes bibliographical references.

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35

Sundaram, Anand R. K. "Vowel recognition using Kohonen's self-organizing feature maps /." Online version of thesis, 1991. http://hdl.handle.net/1850/10710.

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36

Tuerk, Christine M. "Automatic speech synthesis using auditory transforms and artificial neural networks." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385362.

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37

Wieworka, Adam. "Speech recognition using Hidden Markov Models with exponential interpolation of state parameters." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286612.

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38

Balakrishnan, Sreeram Viswanath. "Solving combinatorial optimization problems using neural networks with applications in speech recognition." Thesis, University of Cambridge, 1992. https://www.repository.cam.ac.uk/handle/1810/283679.

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39

Delmege, James W. "CLASS : a study of methods for coarse phonetic classification /." Online version of thesis, 1988. http://hdl.handle.net/1850/10449.

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40

Moore, John Humphrey. "Digitizing human faces for the analysis and synthesis of visible speech." Thesis, Leeds Beckett University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277886.

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41

Ravindran, Sourabh. "Physiologically Motivated Methods For Audio Pattern Classification." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14066.

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Human-like performance by machines in tasks of speech and audio processing has remained an elusive goal. In an attempt to bridge the gap in performance between humans and machines there has been an increased effort to study and model physiological processes. However, the widespread use of biologically inspired features proposed in the past has been hampered mainly by either the lack of robustness across a range of signal-to-noise ratios or the formidable computational costs. In physiological systems, sensor processing occurs in several stages. It is likely the case that signal features and bio

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42

Altun, Halis. "Evaluation of neural learning in a MLP NN for an acoustic-to-articulatory mapping problem using different training pattern vector characteristics." Thesis, University of Nottingham, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263405.

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43

Arriola, Yosu. "Integration of multi-layer perception and hidden Markov models for automatic speech recognition." Thesis, Staffordshire University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292239.

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44

Jantan, Adznan Bin. "A comparative study of various analysis techniques for use in speech recognition systems." Thesis, Swansea University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292473.

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45

Purdy, Trevor. "A Dynamic Vocabulary Speech Recognizer Using Real-Time, Associative-Based Learning." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/942.

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Conventional speech recognizers employ a training phase during which many of their parameters are configured - including vocabulary selection, feature selection, and decision mechanism tailoring to these selections. After this stage during normal operation, these traditional recognizers do not significantly alter any of these parameters. Conversely this work draws heavily on high level human thought patterns and speech perception to outline a set of precepts to eliminate this training phase and instead opt to perform all its tasks during the normal operation. A feature space model is dis

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46

Jeon, Woojay. "Speech Analysis and Cognition Using Category-Dependent Features in a Model of the Central Auditory System." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14061.

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It is well known that machines perform far worse than humans in recognizing speech and audio, especially in noisy environments. One method of addressing this issue of robustness is to study physiological models of the human auditory system and to adopt some of its characteristics in computers. As a first step in studying the potential benefits of an elaborate computational model of the primary auditory cortex (A1) in the central auditory system, we qualitatively and quantitatively validate the model under existing speech processing recognition methodology. Next, we develop new insights and ide

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47

Rochford, Matthew. "Visual Speech Recognition Using a 3D Convolutional Neural Network." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2109.

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Main stream automatic speech recognition (ASR) makes use of audio data to identify spoken words, however visual speech recognition (VSR) has recently been of increased interest to researchers. VSR is used when audio data is corrupted or missing entirely and also to further enhance the accuracy of audio-based ASR systems. In this research, we present both a framework for building 3D feature cubes of lip data from videos and a 3D convolutional neural network (CNN) architecture for performing classification on a dataset of 100 spoken words, recorded in an uncontrolled envi- ronment. Our 3D-CNN ar

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48

Combrinck, Hendrik Petrus. "A cost, complexity and performance comparison of two automatic language identification architectures." Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-12212006-141335/.

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49

Agaiby, Hany. "Word boundary detection for engineering applications." Thesis, University of the West of Scotland, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265933.

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50

Dean, David Brendan. "Synchronous HMMs for audio-visual speech processing." Queensland University of Technology, 2008. http://eprints.qut.edu.au/17689/.

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Both human perceptual studies and automaticmachine-based experiments have shown that visual information from a speaker's mouth region can improve the robustness of automatic speech processing tasks, especially in the presence of acoustic noise. By taking advantage of the complementary nature of the acoustic and visual speech information, audio-visual speech processing (AVSP) applications can work reliably in more real-world situations than would be possible with traditional acoustic speech processing applications. The two most prominent applications of AVSP for viable human-computer-interfaces

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