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Dissertations / Theses on the topic 'Automatic speech recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Alcaraz, Meseguer Noelia. "Speech Analysis for Automatic Speech Recognition." Thesis, Norwegian University of Science and Technology, Department of Electronics and Telecommunications, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9092.

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<p>The classical front end analysis in speech recognition is a spectral analysis which parametrizes the speech signal into feature vectors; the most popular set of them is the Mel Frequency Cepstral Coefficients (MFCC). They are based on a standard power spectrum estimate which is first subjected to a log-based transform of the frequency axis (mel- frequency scale), and then decorrelated by using a modified discrete cosine transform. Following a focused introduction on speech production, perception and analysis, this paper gives a study of the implementation of a speech generative model; where

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2

Gabriel, Naveen. "Automatic Speech Recognition in Somali." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166216.

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The field of speech recognition during the last decade has left the research stage and found its way into the public market, and today, speech recognition software is ubiquitous around us. An automatic speech recognizer understands human speech and represents it as text. Most of the current speech recognition software employs variants of deep neural networks. Before the deep learning era, the hybrid of hidden Markov model and Gaussian mixture model (HMM-GMM) was a popular statistical model to solve speech recognition. In this thesis, automatic speech recognition using HMM-GMM was trained on So

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3

Al-Shareef, Sarah. "Conversational Arabic Automatic Speech Recognition." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/10145/.

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Colloquial Arabic (CA) is the set of spoken variants of modern Arabic that exist in the form of regional dialects and are considered generally to be mother-tongues in those regions. CA has limited textual resource because it exists only as a spoken language and without a standardised written form. Normally the modern standard Arabic (MSA) writing convention is employed that has limitations in phonetically representing CA. Without phonetic dictionaries the pronunciation of CA words is ambiguous, and can only be obtained through word and/or sentence context. Moreover, CA inherits the MSA complex

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4

Jalalvand, Shahab. "Automatic Speech Recognition Quality Estimation." Doctoral thesis, Università degli studi di Trento, 2017. https://hdl.handle.net/11572/368743.

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Evaluation of automatic speech recognition (ASR) systems is difficult and costly, since it requires manual transcriptions. This evaluation is usually done by computing word error rate (WER) that is the most popular metric in ASR community. Such computation is doable only if the manual references are available, whereas in the real-life applications, it is a too rigid condition. A reference-free metric to evaluate the ASR performance is \textit{confidence measure} which is provided by the ASR decoder. However, the confidence measure is not always available, especially in commercial ASR usages. E

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5

Jalalvand, Shahab. "Automatic Speech Recognition Quality Estimation." Doctoral thesis, University of Trento, 2017. http://eprints-phd.biblio.unitn.it/2058/1/PhD_Thesis.pdf.

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Evaluation of automatic speech recognition (ASR) systems is difficult and costly, since it requires manual transcriptions. This evaluation is usually done by computing word error rate (WER) that is the most popular metric in ASR community. Such computation is doable only if the manual references are available, whereas in the real-life applications, it is a too rigid condition. A reference-free metric to evaluate the ASR performance is \textit{confidence measure} which is provided by the ASR decoder. However, the confidence measure is not always available, especially in commercial ASR usages. E

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6

Wang, Peidong. "Robust Automatic Speech Recognition By Integrating Speech Separation." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619099401042668.

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7

Seward, Alexander. "Efficient Methods for Automatic Speech Recognition." Doctoral thesis, KTH, Tal, musik och hörsel, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3675.

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This thesis presents work in the area of automatic speech recognition (ASR). The thesis focuses on methods for increasing the efficiency of speech recognition systems and on techniques for efficient representation of different types of knowledge in the decoding process. In this work, several decoding algorithms and recognition systems have been developed, aimed at various recognition tasks. The thesis presents the KTH large vocabulary speech recognition system. The system was developed for online (live) recognition with large vocabularies and complex language models. The system utilizes weight

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8

Vipperla, Ravichander. "Automatic Speech Recognition for ageing voices." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5725.

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With ageing, human voices undergo several changes which are typically characterised by increased hoarseness, breathiness, changes in articulatory patterns and slower speaking rate. The focus of this thesis is to understand the impact of ageing on Automatic Speech Recognition (ASR) performance and improve the ASR accuracies for older voices. Baseline results on three corpora indicate that the word error rates (WER) for older adults are significantly higher than those of younger adults and the decrease in accuracies is higher for males speakers as compared to females. Acoustic parameters such as

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9

Guzy, Julius Jonathan. "Automatic speech recognition : a refutation approach." Thesis, De Montfort University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.254196.

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10

Deterding, David Henry. "Speaker normalisation for automatic speech recognition." Thesis, University of Cambridge, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359822.

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11

Badr, Ibrahim. "Pronunciation learning for automatic speech recognition." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66022.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 99-101).<br>In many ways, the lexicon remains the Achilles heel of modern automatic speech recognizers (ASRs). Unlike stochastic acoustic and language models that learn the values of their parameters from training data, the baseform pronunciations of words in an ASR vocabulary are typically specified manually, and do not change, unless they are edited by an expert. Our work presents a novel generati

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12

Chen, Chia-Ping. "Noise robustness in automatic speech recognition /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/5829.

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13

Uebler, Ulla. "Multilingual speech recognition /." Berlin : Logos Verlag, 2000. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=009117880&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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14

Evans, N. W. D. "Spectral subtraction for speech enhancement and automatic speech recognition." Thesis, Swansea University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.636935.

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The contributions made in this thesis relate to an extensive investigation of spectral subtraction in the context of speech enhancement and noise robust automatic speech recognition (ASR) and the morphological processing of speech spectrograms. Three sources of error in a spectral subtraction approach are identified and assessed with ASR. The effects of phase, cross-term component and spectral magnitude errors are assessed in a common spectral subtraction framework. ASR results confirm that, except for extreme noise conditions, phase and cross-term component errors are relatively negligible co

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15

Zhang, Xiaozheng. "Automatic speechreading for improved speech recognition and speaker verification." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/13067.

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16

Ragni, Anton. "Discriminative models for speech recognition." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.707926.

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17

Couper, Kenney Fiona. "Automatic determination of sub-word units for automatic speech recognition." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/2788.

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Current automatic speech recognition (ASR) research is focused on recognition of continuous, spontaneous speech. Spontaneous speech contains a lot of variability in the way words are pronounced, and canonical pronunciations of each word are not true to the variation that is seen in real data. Two of the components of an ASR system are acoustic models and pronunciation models. The variation within spontaneous speech must be accounted for by these components. Phones, or context-dependent phones are typically used as the base subword unit, and one acoustic model is trained for each sub-word unit.

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18

Thambiratnam, David P. "Speech recognition in adverse environments." Thesis, Queensland University of Technology, 1999. https://eprints.qut.edu.au/36099/1/36099_Thambiratnam_1999.pdf.

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This thesis presents a study of techniques used to improve the performance of small vocabulary isolated word speaker dependent automatic speech recognition systems in adverse environments. Such systems are applicable to 'command and control' applications, for example industrial applications where machines are controlled by voice, providing hands-free and eyes-free operation. Adverse environments present the largest obstacle to the deployment of accurate and usable speech recognition systems. This is because they cause discrepancies between training and testing environments. Two solutions to t

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19

Tabani, Hamid. "Low-power architectures for automatic speech recognition." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/462249.

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Automatic Speech Recognition (ASR) is one of the most important applications in the area of cognitive computing. Fast and accurate ASR is emerging as a key application for mobile and wearable devices. These devices, such as smartphones, have incorporated speech recognition as one of the main interfaces for user interaction. This trend towards voice-based user interfaces is likely to continue in the next years which is changing the way of human-machine interaction. Effective speech recognition systems require real-time recognition, which is challenging for mobile devices due to the compute-in

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20

Martínez, del Hoyo Canterla Alfonso. "Design of Detectors for Automatic Speech Recognition." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-16548.

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This thesis presents methods and results for optimizing subword detectors in continuous speech. Speech detectors are useful within areas like detection-based ASR, pronunciation training, phonetic analysis, word spotting, etc. Firstly, we propose a structure suitable for subword detection. This structure is based on the standard HMM framework, but in each detector the MFCC feature extractor and the models are trained for the specific detection problem. Our experiments in the TIMIT database validate the effectiveness of this structure for detection of phones and articulatory features. Secondly,

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21

Bengio, Yoshua. "Connectionist models applied to automatic speech recognition." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63920.

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22

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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23

Austin, Stephen Christopher. "Hidden Markov models for automatic speech recognition." Thesis, University of Cambridge, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292913.

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24

Frankel, Joe. "Linear dynamic models for automatic speech recognition." Thesis, University of Edinburgh, 2004. http://hdl.handle.net/1842/1087.

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The majority of automatic speech recognition (ASR) systems rely on hidden Markov models (HMM), in which the output distribution associated with each state is modelled by a mixture of diagonal covariance Gaussians. Dynamic information is typically included by appending time-derivatives to feature vectors. This approach, whilst successful, makes the false assumption of framewise independence of the augmented feature vectors and ignores the spatial correlations in the parametrised speech signal. This dissertation seeks to address these shortcomings by exploring acoustic modelling for ASR with an

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25

Gu, Y. "Perceptually-based features in automatic speech recognition." Thesis, Swansea University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.637182.

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Interspeaker variability of speech features is one of most important problems in automatic speech recognition (ASR), and makes speaker-independent systems much more difficult to achieve than speaker-dependent ones. The work described in the Thesis examines two ideas to overcome this problem. The first attempts to extract more reliable speech features by perceptually-based modelling; the second investigates the speaker variability in this speech feature and reduces its effects by a speaker normalisation scheme. The application of human speech perception in automatic speech recognition is discus

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26

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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27

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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28

Chan, Carlos Chun Ming. "Speaker model adaptation in automatic speech recognition." Thesis, Robert Gordon University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339307.

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29

Duchnowski, Paul. "A new structure for automatic speech recognition." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/17333.

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Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993.<br>Includes bibliographical references (leaves 102-110).<br>by Paul Duchnowski.<br>Sc.D.

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30

Wang, Stanley Xinlei. "Using graphone models in automatic speech recognition." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53114.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>Includes bibliographical references (p. 87-90).<br>This research explores applications of joint letter-phoneme subwords, known as graphones, in several domains to enable detection and recognition of previously unknown words. For these experiments, graphones models are integrated into the SUMMIT speech recognition framework. First, graphones are applied to automatically generate pronunciations of restaurant names for a speech recognizer. Word recognition evaluations show that

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31

Seigel, Matthew Stephen. "Confidence estimation for automatic speech recognition hypotheses." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648633.

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32

Abdelhamied, Kadry A. "Automatic identification and recognition of deaf speech /." The Ohio State University, 1986. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487266691094027.

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33

Cherri, Mona Youssef 1956. "Automatic Speech Recognition Using Finite Inductive Sequences." Thesis, University of North Texas, 1996. https://digital.library.unt.edu/ark:/67531/metadc277749/.

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This dissertation addresses the general problem of recognition of acoustic signals which may be derived from speech, sonar, or acoustic phenomena. The specific problem of recognizing speech is the main focus of this research. The intention is to design a recognition system for a definite number of discrete words. For this purpose specifically, eight isolated words from the T1MIT database are selected. Four medium length words "greasy," "dark," "wash," and "water" are used. In addition, four short words are considered "she," "had," "in," and "all." The recognition system addresses the following

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34

Colton, Larry Don. "Confidence and rejection in automatic speech recognition /." Full text open access at:, 1997. http://content.ohsu.edu/u?/etd,21.

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35

Li, Jinyu. "Soft margin estimation for automatic speech recognition." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26613.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Dr. Chin-Hui Lee; Committee Member: Dr. Anthony Joseph Yezzi; Committee Member: Dr. Biing-Hwang (Fred) Juang; Committee Member: Dr. Mark Clements; Committee Member: Dr. Ming Yuan. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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36

Principi, Emanuele, and Emanuele Principi. "Pre-processing techniques for automatic speech recognition." Doctoral thesis, Università Politecnica delle Marche, 2009. http://hdl.handle.net/11566/242152.

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37

Lebart, Katia. "Speech dereverberation applied to automatic speech recognition and hearing aids." Thesis, University of Sussex, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285064.

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38

LEBART, KATIA. "Speech dereverberation applied to automatic speech recognition and hearing aids." Rennes 1, 1999. http://www.theses.fr/1999REN10033.

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Cette these concerne la dereverberation de la parole dans les contextes specifiques de l'application aux appareils pour malentendants ou a la reconnaissance automatique de la parole. Les methodes considerees doivent etre fonctionnelles dans des conditions ou les canaux acoustiques pris en compte sont inconnus et variables. Nous proposons donc de discriminer la reverberation du signal direct a l'aide de proprietes de la reverberation qui sont independantes du canal acoustique. La correlation spatiale des signaux, leurs directions de provenance et leurs supports temporels menent a differentes me

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39

Johnston, Samuel John Charles, and Samuel John Charles Johnston. "An Approach to Automatic and Human Speech Recognition Using Ear-Recorded Speech." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/625626.

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Speech in a noisy background presents a challenge for the recognition of that speech both by human listeners and by computers tasked with understanding human speech (automatic speech recognition; ASR). Years of research have resulted in many solutions, though none so far have completely solved the problem. Current solutions generally require some form of estimation of the noise, in order to remove it from the signal. The limitation is that noise can be highly unpredictable and highly variable, both in form and loudness. The present report proposes a method of recording a speech signal in a

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40

Arrowood, Jon A. "Using observation uncertainty for robust speech recognition." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04082004-180005/unrestricted/arrowood%5Fjon%5Fa%5F200312%5Fphd.pdf.

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41

Gillespie, Bradford W. "Strategies for improving audible quality and speech recognition accuracy of reverberant speech /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/5930.

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42

Wrede, Britta. "Modelling the effects of speech rate variation for automatic speech recognition." [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=969765304.

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43

Wilkinson, Nicholas. "Modelling asynchrony in the articulation of speech for automatic speech recognition." Thesis, University of Birmingham, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399032.

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Current automatic speech recognition systems make the assumption that all the articulators in the vocal tract move in synchrony with one another to produce speech. This thesis describes the development of a more realistic model that allows some asynchrony between the articulators with the aim of improving speech recognition accuracy. Experiments on the TEVHT database demonstrate that higher phone recognition accuracy is obtained by separate modelling of the voiced and voiceless components of speech by splitting the speech spectrum into high and low frequency bands. To model further articulator

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44

Livescu, Karen 1975. "Analysis and modeling of non-native speech for automatic speech recognition." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80204.

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45

Kocour, Martin. "Automatic Speech Recognition System Continually Improving Based on Subtitled Speech Data." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-399164.

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V dnešnej dobe systémy rozpoznávania reči s veľkým slovníkom dosahujú pomerne vysoké presnosti. Za ich výsledkami však často stoja desiatky ba až stovky hodín manuálne oanotovaných trénovacích dát. Takéto dáta sú často bežne nedostupné alebo pre požadovaný jazyk vôbec neexistujú. Možným riešením je použitie bežne dostupných no menej kvalitných audiovizuálnych dát. Táto práca sa zaoberá technikou zpracovania práve takýchto dát a ich použitím pre trénovanie akustických modelov. Ďalej táto práca pojednáva o možnom využití týchto dát pre kontinuálne vylepšovanie modelov, kedže tieto dáta sú prakti

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46

Nel, Pieter Willem. "Automatic syllabification of untranscribed speech." Thesis, Stellenbosch : Stellenbosch University, 2005. http://hdl.handle.net/10019.1/50285.

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Thesis (MScEng)--Stellenbosch University, 2005.<br>ENGLISH ABSTRACT: The syllable has been proposed as a unit of automatic speech recognition due to its strong links with human speech production and perception. Recently, it has been proved that incorporating information from syllable-length time-scales into automatic speech recognition improves results in large vocabulary recognition tasks. It was also shown to aid in various language recognition tasks and in foreign accent identification. Therefore, the ability to automatically segment speech into syllables is an important research tool.

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47

Kleinschmidt, Tristan Friedrich. "Robust speech recognition using speech enhancement." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/31895/1/Tristan_Kleinschmidt_Thesis.pdf.

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Automatic Speech Recognition (ASR) has matured into a technology which is becoming more common in our everyday lives, and is emerging as a necessity to minimise driver distraction when operating in-car systems such as navigation and infotainment. In “noise-free” environments, word recognition performance of these systems has been shown to approach 100%, however this performance degrades rapidly as the level of background noise is increased. Speech enhancement is a popular method for making ASR systems more ro- bust. Single-channel spectral subtraction was originally designed to improve hu- man

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48

Wolf, Martin. "Channel selection and reverberation-robust automatic speech recognition." Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/134806.

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If speech is acquired by a close-talking microphone in a controlled and noise-free environment, current state-of-the-art recognition systems often show an acceptable error rate. The use of close-talking microphones, however, may be too restrictive in many applications. Alternatively, distant-talking microphones, often placed several meters far from the speaker, may be used. Such setup is less intrusive, since the speaker does not have to wear any microphone, but the Automatic Speech Recognition (ASR) performance is strongly affected by noise and reverberation. The thesis is focused on ASR appl

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49

Zhang, Xiaojia. "Language modeling for automatic speech recognition in telehealth." Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/4245.

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Thesis (M.S.)--University of Missouri-Columbia, 2005.<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 (January 11, 2007) Vita. Includes bibliographical references.

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50

Sklar, Alexander Gabriel. "Channel Modeling Applied to Robust Automatic Speech Recognition." Scholarly Repository, 2007. http://scholarlyrepository.miami.edu/oa_theses/87.

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In automatic speech recognition systems (ASRs), training is a critical phase to the system?s success. Communication media, either analog (such as analog landline phones) or digital (VoIP) distort the speaker?s speech signal often in very complex ways: linear distortion occurs in all channels, either in the magnitude or phase spectrum. Non-linear but time-invariant distortion will always appear in all real systems. In digital systems we also have network effects which will produce packet losses and delays and repeated packets. Finally, one cannot really assert what path a signal will take, and

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