Dissertations / Theses on the topic 'Speaker recognition systems'
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Neville, Katrina Lee, and katrina neville@rmit edu au. "Channel Compensation for Speaker Recognition Systems." RMIT University. Electrical and Computer Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080514.093453.
Full textDu, Toit Ilze. "Non-acoustic speaker recognition." Thesis, Stellenbosch : University of Stellenbosch, 2004. http://hdl.handle.net/10019.1/16315.
Full textENGLISH ABSTRACT: In this study the phoneme labels derived from a phoneme recogniser are used for phonetic speaker recognition. The time-dependencies among phonemes are modelled by using hidden Markov models (HMMs) for the speaker models. Experiments are done using firstorder and second-order HMMs and various smoothing techniques are examined to address the problem of data scarcity. The use of word labels for lexical speaker recognition is also investigated. Single word frequencies are counted and the use of various word selections as feature sets are investigated. During April 2004, the University of Stellenbosch, in collaboration with Spescom DataVoice, participated in an international speaker verification competition presented by the National Institute of Standards and Technology (NIST). The University of Stellenbosch submitted phonetic and lexical (non-acoustic) speaker recognition systems and a fused system (the primary system) that fuses the acoustic system of Spescom DataVoice with the non-acoustic systems of the University of Stellenbosch. The results were evaluated by means of a cost model. Based on the cost model, the primary system obtained second and third position in the two categories that were submitted.
AFRIKAANSE OPSOMMING: Hierdie projek maak gebruik van foneem-etikette wat geklassifiseer word deur ’n foneemherkenner en daarna gebruik word vir fonetiese sprekerherkenning. Die tyd-afhanklikhede tussen foneme word gemodelleer deur gebruik te maak van verskuilde Markov modelle (HMMs) as sprekermodelle. Daar word ge¨eksperimenteer met eerste-orde en tweede-orde HMMs en verskeie vergladdingstegnieke word ondersoek om dataskaarsheid aan te spreek. Die gebruik van woord-etikette vir sprekerherkenning word ook ondersoek. Enkelwoordfrekwensies word getel en daar word ge¨eksperimenteer met verskeie woordseleksies as kenmerke vir sprekerherkenning. Gedurende April 2004 het die Universiteit van Stellenbosch in samewerking met Spescom DataVoice deelgeneem aan ’n internasionale sprekerverifikasie kompetisie wat deur die National Institute of Standards and Technology (NIST) aangebied is. Die Universiteit van Stellenbosch het ingeskryf vir ’n fonetiese en ’n woordgebaseerde (nie-akoestiese) sprekerherkenningstelsel, asook ’n saamgesmelte stelsel wat as primˆere stelsel dien. Die saamgesmelte stelsel is ’n kombinasie van Spescom DataVoice se akoestiese stelsel en die twee nie-akoestiese stelsels van die Universiteit van Stellenbosch. Die resultate is ge¨evalueer deur gebruik te maak van ’n koste-model. Op grond van die koste-model het die primˆere stelsel tweede en derde plek behaal in die twee kategorie¨e waaraan deelgeneem is.
Shou-Chun, Yin 1980. "Speaker adaptation in joint factor analysis based text independent speaker verification." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=100735.
Full textUzuner, Halil. "Robust text-independent speaker recognition over telecommunications systems." Thesis, University of Surrey, 2006. http://epubs.surrey.ac.uk/843391/.
Full textWildermoth, Brett Richard, and n/a. "Text-Independent Speaker Recognition Using Source Based Features." Griffith University. School of Microelectronic Engineering, 2001. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20040831.115646.
Full textWildermoth, Brett Richard. "Text-Independent Speaker Recognition Using Source Based Features." Thesis, Griffith University, 2001. http://hdl.handle.net/10072/366289.
Full textThesis (Masters)
Master of Philosophy (MPhil)
School of Microelectronic Engineering
Faculty of Engineering and Information Technology
Full Text
Adami, André Gustavo. "Modeling prosodic differences for speaker and language recognition /." Full text open access at:, 2004. http://content.ohsu.edu/u?/etd,19.
Full textYu, K. P. "Text dependency and adaptation in training speaker recognition systems." Thesis, Swansea University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.636721.
Full textWark, Timothy J. "Multi-modal speech processing for automatic speaker recognition." Thesis, Queensland University of Technology, 2001.
Find full textMathan, Luc Stefan. "Speaker-independent access to a large lexicon." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63773.
Full textHuliehel, Fakhralden A. "An RBFN-based system for speaker-independent speech recognition." Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-06062008-162619/.
Full textCastellano, Pierre John. "Speaker recognition modelling with artificial neural networks." Thesis, Queensland University of Technology, 1997.
Find full textSepasian, Mojtaba. "Multibiometric security in wireless communication systems." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/5081.
Full textChan, Siu Man. "Improved speaker verification with discrimination power weighting /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202004%20CHANS.
Full textIncludes bibliographical references (leaves 86-93). Also available in electronic version. Access restricted to campus users.
Slomka, Stefan. "Multiple classifier structures for automatic speaker recognition under adverse conditions." Thesis, Queensland University of Technology, 1999.
Find full textCilliers, Francois Dirk. "Tree-based Gaussian mixture models for speaker verification." Thesis, Link to the online version, 2005. http://hdl.handle.net/10019.1/1639.
Full textCampanelli, Michael R. "Computer classification of stop consonants in a speaker independent continuous speech environment /." Online version of thesis, 1991. http://hdl.handle.net/1850/11051.
Full textReynolds, Douglas A. "A Gaussian mixture modeling approach to text-independent speaker identification." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/16903.
Full textPool, Jan. "Investigation of the impact of high frequency transmitted speech on speaker recognition." Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/52895.
Full textSome digitised pages may appear illegible due to the condition of the original hard copy.
ENGLISH ABSTRACT: Speaker recognition systems have evolved to a point where near perfect performance can be obtained under ideal conditions, even if the system must distinguish between a large number of speakers. Under adverse conditions, such as when high noise levels are present or when the transmission channel deforms the speech, the performance is often less than satisfying. This project investigated the performance of a popular speaker recognition system, that use Gaussian mixture models, on speech transmitted over a high frequency channel. Initial experiments demonstrated very unsatisfactory results for the base line system. We investigated a number of robust techniques. We implemented and applied some of them in an attempt to improve the performance of the speaker recognition systems. The techniques we tested showed only slight improvements. We also investigates the effects of a high frequency channel and single sideband modulation on the speech features of speech processing systems. The effects that can deform the features, and therefore reduce the performance of speech systems, were identified. One of the effects that can greatly affect the performance of a speech processing system is noise. We investigated some speech enhancement techniques and as a result we developed a new statistical based speech enhancement technique that employs hidden Markov models to represent the clean speech process.
AFRIKAANSE OPSOMMING: Sprekerherkenning-stelsels het 'n punt bereik waar nabyaan perfekte resultate verwag kan word onder ideale kondisies, selfs al moet die stelsel tussen 'n groot aantal sprekers onderskei. Wanneer nie-ideale kondisies, soos byvoorbeeld hoë ruisvlakke of 'n transmissie kanaal wat die spraak vervorm, teenwoordig is, is die resultate gewoonlik nie bevredigend nie. Die projek ondersoek die werksverrigting van 'n gewilde sprekerherkenning-stelsel, wat gebruik maak van Gaussiese mengselmodelle, op spraak wat oor 'n hoë frekwensie transmissie kanaal gestuur is. Aanvanklike eksperimente wat gebruik maak van 'n basiese stelsel het nie goeie resultate opgelewer nie. Ons het 'n aantal robuuste tegnieke ondersoek en 'n paar van hulle geïmplementeer en getoets in 'n poging om die resultate van die sprekerherkenning-stelsel te verbeter. Die tegnieke wat ons getoets het, het net geringe verbetering getoon. Die studie het ook die effekte wat die hoë-frekwensie kanaal en enkel-syband modulasie op spraak kenmerkvektore, ondersoek. Die effekte wat die spraak kenmerkvektore kan vervorm en dus die werkverrigting van spraak stelsels kan verlaag, is geïdentifiseer. Een van die effekte wat 'n groot invloed op die werkverrigting van spraakstelsels het, is ruis. Ons het spraak verbeterings metodes ondersoek en dit het gelei tot die ontwikkeling van 'n statisties gebaseerde spraak verbeteringstegniek wat gebruik maak van verskuilde Markov modelle om die skoon spraakproses voor te stel.
Tsao, Yu. "An ensemble speaker and speaking environment modeling approach to robust speech recognition." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26540.
Full textCommittee Chair: Lee, Chin-Hui; Committee Member: Anthony Joseph Yezzi; Committee Member: Biing-Hwang (Fred) Juang; Committee Member: Mark Clements; Committee Member: Ming Yuan. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Brummer, Niko. "Measuring, refining and calibrating speaker and language information extracted from speech." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/5139.
Full textENGLISH ABSTRACT: We propose a new methodology, based on proper scoring rules, for the evaluation of the goodness of pattern recognizers with probabilistic outputs. The recognizers of interest take an input, known to belong to one of a discrete set of classes, and output a calibrated likelihood for each class. This is a generalization of the traditional use of proper scoring rules to evaluate the goodness of probability distributions. A recognizer with outputs in well-calibrated probability distribution form can be applied to make cost-effective Bayes decisions over a range of applications, having di fferent cost functions. A recognizer with likelihood output can additionally be employed for a wide range of prior distributions for the to-be-recognized classes. We use automatic speaker recognition and automatic spoken language recognition as prototypes of this type of pattern recognizer. The traditional evaluation methods in these fields, as represented by the series of NIST Speaker and Language Recognition Evaluations, evaluate hard decisions made by the recognizers. This makes these recognizers cost-and-prior-dependent. The proposed methodology generalizes that of the NIST evaluations, allowing for the evaluation of recognizers which are intended to be usefully applied over a wide range of applications, having variable priors and costs. The proposal includes a family of evaluation criteria, where each member of the family is formed by a proper scoring rule. We emphasize two members of this family: (i) A non-strict scoring rule, directly representing error-rate at a given prior. (ii) The strict logarithmic scoring rule which represents information content, or which equivalently represents summarized error-rate, or expected cost, over a wide range of applications. We further show how to form a family of secondary evaluation criteria, which by contrasting with the primary criteria, form an analysis of the goodness of calibration of the recognizers likelihoods. Finally, we show how to use the logarithmic scoring rule as an objective function for the discriminative training of fusion and calibration of speaker and language recognizers.
AFRIKAANSE OPSOMMING: Ons wys hoe om die onsekerheid in die uittree van outomatiese sprekerherkenning- en taalherkenningstelsels voor te stel, te meet, te kalibreer en te optimeer. Dit maak die bestaande tegnologie akkurater, doeltre ender en meer algemeen toepasbaar.
Phythian, Mark. "Speaker identification for forensic applications." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36079/3/__qut.edu.au_Documents_StaffHome_StaffGroupR%24_rogersjm_Desktop_36079_Digitised%20Thesis.pdf.
Full textFanner, Robert M. "Analysis and implementation of the speaker adaptation techniques : MAP, MLLR, and MLED." Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/52653.
Full textENGLISH ABSTRACT: The topic of this thesis is speaker adaptation, whereby speaker-independent speech models are adapted to more closely match individual speakers by utilising a small amount of data from the targeted individual. Speaker adaptation methods - specifically, the MAP, MLLR and MLED speaker adaptation methods - are critically evaluated and compared. Two novel extensions of the MLED adaptation method are introduced, derived and evaluated. The first incorporates the explicit modelling of the mean speaker model in the speaker-space into the MLED framework. The second extends MLED to use basis vectors modelling inter-class variance for classes of speech models, instead of basis vectors modelling inter-speaker variance. An evaluation of the effect of two different types of feature vector - PLP-cepstra and LPCCs - on the performance of speaker adaptation is made, to determine which feature vector is optimal for speaker-independent systems and the adaptation thereof.
AFRIKAANSE OPSOMMING: Die onderwerp van hierdie tesis is spreker-aanpassing, dit wil sê, die verandering van 'n spreker-onafhanklike spraakmodel om nader aan 'n spreker-afhanklike model vir 'n individu te wees, gegewe 'n klein hoeveelheid spraakdata van die individu. Die volgende sprekeraanpassing-metodes word geëvalueer: MAP, MLLR en MLED. Twee nuwe uitbreidings vir die MLED-metode word beskryf, afgelei en geëvalueer. Die eerste inkorporeer die eksplisiete modellering van die gemiddelde sprekermodel van die sprekerruimte in die MLED metode. Die tweede uitbreiding maak gebruik van basisvektore vir MLED wat vanaf die interklas-variansie tussen 'n stel sprekerklasse in plaas van die interspreker-variansie afgelei is. Die effek van twee tipes kenmerk-vektore - PLP-kepstra en LPCC's - op die prestasie van sprekeraanpassings-metodes word ondersoek, sodat die optimale tipe kenmerk-vektor vir spreker-onafhanklike modelle en hul aanpassing gevind kan word.
Barger, Peter James. "Speech processing for forensic applications." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36081/1/36081_Barger_1998.pdf.
Full textTomashenko, Natalia. "Speaker adaptation of deep neural network acoustic models using Gaussian mixture model framework in automatic speech recognition systems." Thesis, Le Mans, 2017. http://www.theses.fr/2017LEMA1040/document.
Full textDifferences between training and testing conditions may significantly degrade recognition accuracy in automatic speech recognition (ASR) systems. Adaptation is an efficient way to reduce the mismatch between models and data from a particular speaker or channel. There are two dominant types of acoustic models (AMs) used in ASR: Gaussian mixture models (GMMs) and deep neural networks (DNNs). The GMM hidden Markov model (GMM-HMM) approach has been one of the most common technique in ASR systems for many decades. Speaker adaptation is very effective for these AMs and various adaptation techniques have been developed for them. On the other hand, DNN-HMM AMs have recently achieved big advances and outperformed GMM-HMM models for various ASR tasks. However, speaker adaptation is still very challenging for these AMs. Many adaptation algorithms that work well for GMMs systems cannot be easily applied to DNNs because of the different nature of these models. The main purpose of this thesis is to develop a method for efficient transfer of adaptation algorithms from the GMM framework to DNN models. A novel approach for speaker adaptation of DNN AMs is proposed and investigated. The idea of this approach is based on using so-called GMM-derived features as input to a DNN. The proposed technique provides a general framework for transferring adaptation algorithms, developed for GMMs, to DNN adaptation. It is explored for various state-of-the-art ASR systems and is shown to be effective in comparison with other speaker adaptation techniques and complementary to them
PINHEIRO, Hector Natan Batista. "Verificação de locutores independente de texto: uma análise de robustez a ruído." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/18045.
Full textMade available in DSpace on 2016-11-08T19:13:18Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_Final.pdf: 15901621 bytes, checksum: e3bd1c1be70941932d970f61be02e4c1 (MD5) Previous issue date: 2015-02-25
O processo de identificação de um determinado indivíduo é realizado milhões de vezes, todos os dias, por organizações dos mais diversos setores. Perguntas como "Quem é esse indivíduo?" ou "É essa pessoa quem ela diz ser?" são realizadas frequentemente por organizações financeiras, sistemas de saúde, sistemas de comércio eletrônico, sistemas de telecomunicações e por instituições governamentais. Identificação biométrica diz respeito ao processo de realizar essa identificação a partir de características físicas ou comportamentais. Tais características são comumente referenciadas como características biométricas e alguns exemplos delas são: face, impressão digital, íris, assinatura e voz. Reconhecimento de locutores é uma modalidade biométrica que se propõe a realizar o processo de identificação pessoal a partir das informações presentes unicamente na voz do indivíduo. Este trabalho foca no desenvolvimento de sistemas de verificação de locutores independente de texto. O principal desafio no desenvolvimento desses sistemas provém das chamadas incompatibilidades que podem ocorrer na aquisição dos sinais de voz. As técnicas propostas para suavizá-las são chamadas de técnicas de compensação e três são os domínios onde elas podem operar: no processo de extração de características do sinal, na construção dos modelos dos locutores e no cálculo do score final do sistema. Além de apresentar uma vasta revisão da literatura do desenvolvimento de sistemas de verificação de locutores independentes de texto, esse trabalho também apresenta as principais técnicas de compensação de características, modelos e scores. Na fase de experimentação, uma análise comparativa das principais técnicas propostas na literatura é apresentada. Além disso, duas técnicas de compensação são propostas, uma do domínio de modelagem e outra do domínio dos scores. A técnica de compensação de score proposta é baseada na Distribuição Normal Acumulada e apresentou, em alguns contextos, resultados superiores aos apresentados pelas principais técnicas da literatura. Já a técnica de compensação de modelo é baseada em uma técnica da literatura que combina dois conceitos: treinamento multi-condicional e Teoria dos Dados Ausentes (Missing Data Theory). A formulação apresentada pelos autores é baseada nos chamados Modelos de União a Posteriori (Posterior Union Models), mas não é completamente adequada para verificação de locutores independente de texto. Este trabalho apresenta uma formulação apropriada para esse contexto que combina os dois conceitos utilizados pelos autores com um tipo de modelagem utilizando UBMs (Universal Background Models). A técnica proposta apresentou ganhos de desempenhos quando comparada à técnica-padrão GMM-UBM, baseada em Modelos de Misturas Gaussianas (GMMs).
The personal identification of individuals is a task executed millions of times every day by organizations from diverse fields. Questions such as "Who is this individual?" or "Is this person who he or she claims to be?" are constantly made by organizations in financial services, health care, e-commerce, telecommunication systems and governments. Biometric identification is the process of identifying people using their physiological or behavioral characteristics. These characteristics are generally known as biometrics and examples of these include face, fingerprint, iris, handwriting and speech. Speaker recognition is a biometric modality which makes the personal identification by using speaker-specific information from the speech. This work focuses on the development of text-independent speaker verification systems. In these systems, speech from an individual is used to verify the claimed identity of that individual. Furthermore, the verification must occur independently of the pronounced word or phrase. The main challenge in the development of speaker recognition systems comes from the mismatches which may occur in the acquisition of the speech signals. The techniques proposed to mitigate the mismatch effects are referred as compensation methods. They may operate in three domains: in the feature extraction process, in the estimation of the speaker models and in the computation of the decision score. Besides presenting a wide description of the main techniques used in the development of text-independent speaker verification systems, this work presents the description of the main feature-, model- and score-based compensation methods. In the experiments, this work shows comprehensive comparisons between the conventional techniques and the alternatively compensations methods. Furthermore, two compensation methods are proposed: one operates in the model domain and the other in the score-domain. The scoredomain proposed compensation method is based on the Normal cumulative distribution function and, in some contexts, outperformed the performance of the main score-domain compensation techniques. On the other hand, the model-domain compensation technique proposed in this work is based on a method presented in the literature which combines two concepts: the multi-condition training and the Missing Data Theory. The formulation proposed by the authors is based on the Posterior Union models and is not completely appropriate for the text-independent speaker verification task. This work proposes a more appropriate formulation for this context which combines the concepts used by the authors with a type of modeling using Universal Background Models (UBMs). The proposed method outperformed the usual GMM-UBM modeling technique, based on Gaussian Mixture Models (GMMs).
Lucey, Simon. "Audio-visual speech processing." Thesis, Queensland University of Technology, 2002. https://eprints.qut.edu.au/36172/7/SimonLuceyPhDThesis.pdf.
Full textHong, Z. (Zimeng). "Speaker gender recognition system." Master's thesis, University of Oulu, 2017. http://jultika.oulu.fi/Record/nbnfioulu-201706082645.
Full textKamarauskas, Juozas. "Speaker recognition by voice." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2009. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2009~D_20090615_093847-20773.
Full textDisertacijoje nagrinėjami kalbančiojo atpažinimo pagal balsą klausimai. Aptartos kalbančiojo atpažinimo sistemos, jų raida, atpažinimo problemos, požymių sistemos įvairovė bei kalbančiojo modeliavimo ir požymių palyginimo metodai, naudojami nuo ištarto teksto nepriklausomame bei priklausomame kalbančiojo atpažinime. Darbo metu sukurta nuo ištarto teksto nepriklausanti kalbančiojo atpažinimo sistema. Kalbėtojų modelių kūrimui ir požymių palyginimui buvo panaudoti Gauso mišinių modeliai. Pasiūlytas automatinis vokalizuotų garsų išrinkimo (segmentavimo) metodas. Šis metodas yra greitai veikiantis ir nereikalaujantis iš vartotojo jokių papildomų veiksmų, tokių kaip kalbos signalo ir triukšmo pavyzdžių nurodymas. Pasiūlyta požymių vektorių sistema, susidedanti iš žadinimo signalo bei balso trakto parametrų. Kaip žadinimo signalo parametras, panaudotas žadinimo signalo pagrindinis dažnis, kaip balso trakto parametrai, panaudotos keturios formantės bei trys antiformantės. Siekiant suvienodinti žemesnių bei aukštesnių formančių ir antiformančių dispersijas, jas pasiūlėme skaičiuoti melų skalėje. Rezultatų palyginimui sistemoje buvo realizuoti standartiniai požymiai, naudojami kalbos bei asmens atpažinime – melų skalės kepstro koeficientai (MSKK). Atlikti kalbančiojo atpažinimo eksperimentai parodė, kad panaudojus pasiūlytą požymių sistemą buvo gauti geresni atpažinimo rezultatai, nei panaudojus standartinius požymius (MSKK). Gautas lygių klaidų lygis, panaudojant pasiūlytą požymių... [toliau žr. visą tekstą]
Nosratighods, Mohaddeseh Electrical Engineering & Telecommunications Faculty of Engineering UNSW. "Robust speaker verification system." Publisher:University of New South Wales. Electrical Engineering & Telecommunications, 2008. http://handle.unsw.edu.au/1959.4/42796.
Full textTran, Michael. "An approach to a robust speaker recognition system." Diss., This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06062008-164814/.
Full textRamirez, Jose Luis. "Effects of clipping distortion on an Automatic Speaker Recognition system." Thesis, University of Colorado at Denver, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10112619.
Full textClipping distortion is a common problem faced in the audio recording world in which an audio signal is recorded at higher amplitude than the recording system’s limitations, resulting in a portion of the acoustic event not being recorded. Several government agencies employ the use of Automatic Speaker Recognition (ASR) systems in order to identify the speaker of an acquired recording. This is done automatically using a nonbiased approach by running a questioned recording through an ASR system and comparing it to a pre-existing database of voice samples of whom the speakers are known. A matched speaker is indicated by a high correlation of likelihood between the questioned recording and the ones from the known database. It is possible that during the process of making the questioned recording the speaker was speaking too loudly into the recording device, a gain setting was set too high, or there was post-processing done to the point that clipping distortion is introduced into the recording. Clipping distortion results from the amplitude of an audio signal surpassing the maximum sampling value of the recording system. This affects the quantized audio signal by truncating peaks at the max value rather than the actual amplitude of the input signal. In theory clipping distortion will affect likelihood ratios in a negative way between two compared recordings of the same speaker. This thesis will test this hypothesis. Currently there is no research that has helped as a guideline for knowing the limitations when using clipped recordings. This thesis will investigate to what degree of effect will clipped material have on the system performance of a Forensic Automatic Speaker Recognition system.
Necioğlu, Burhan F. "Objectively measured descriptors for perceptual characterization of speakers." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15035.
Full textHeimark, Erlend. "Authentication: From Passwords to Biometrics : An implementation of a speaker recognition system on Android." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for telematikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-19004.
Full textAlamri, Safi S. "Text-independent, automatic speaker recognition system evaluation with males speaking both Arabic and English." Thesis, University of Colorado at Denver, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1605087.
Full textAutomatic speaker recognition is an important key to speaker identification in media forensics and with the increase of cultures mixing, there?s an increase in bilingual speakers all around the world. The purpose of this thesis is to compare text-independent samples of one person using two different languages, Arabic and English, against a single language reference population. The hope is that a design can be started that may be useful in further developing software that can complete accurate text-independent ASR for bilingual speakers speaking either language against a single language reference population. This thesis took an Arabic model sample and compared it against samples that were both Arabic and English using and an Arabic reference population, all collected from videos downloaded from the Internet. All of the samples were text-independent and enhanced to optimal performance. The data was run through a biometric software called BATVOX 4.1, which utilizes the MFCCs and GMM methods of speaker recognition and identification. The result of testing through BATVOX 4.1 was likelihood ratios for each sample that were evaluated for similarities and differences, trends, and problems that had occurred.
Raab, Martin. "Real world approaches for multilingual and non-native speech recognition." Berlin Logos-Verl, 2010. http://d-nb.info/1002021049/04.
Full textAl-Ani, Ahmed Karim. "An improved pattern classification system using optimal feature selection, classifier combination, and subspace mapping techniques." Thesis, Queensland University of Technology, 2002.
Find full textMangayyagari, Srikanth. "Voice recognition system based on intra-modal fusion and accent classification." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002229.
Full textBekli, Zeid, and William Ouda. "A performance measurement of a Speaker Verification system based on a variance in data collection for Gaussian Mixture Model and Universal Background Model." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20122.
Full textCarbonell, Noëlle. "Reconnaissance de la parole continue et dialogue homme-machine : acquisition et mise en œuvre d'expertises." Nancy 1, 1991. http://www.theses.fr/1991NAN10423.
Full textYing-Hao, Chen, and 陳英豪. "Text-independent Speaker Recognition Systems Using GMM in 200 Mandarin Speakers." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/98000557557954695918.
Full text高苑科技大學
電子工程研究所
102
The purpose of this thesis is to establish a text-independent speaker identification system for medium-amount speaker populations.The database is from television recording, containing two hundred voice data. The period of each person's voice is seventy-five seconds.Text-independent speaker identification system operates pros and cons, depending on the rate of accurate identification and the speed of identification systems.In general, the rate of accurate identification is affected by the amount of subject language speakers.This paper classified a large number of databases by vector quantization formula Gaussian mixture model (Vector Quantization Gaussian Mixture Model) so the rate of accurace identification won’t drop off rapidly because of a significant increase in population.We use a independent Gaussian distribution of Gaussian mixture model to simulate a particular speaker feature in extensive voice space.Experimental results show that GMM speaker identification system is outstanding in text-independent pattern.Besides, the concept of pre-grouping makes speaker GMM training much faster than traditional grouping method. It can save up half of the training time.The paper also compares the performance of the diagonal covariance matrix with full covariance matrix. Although the latter's rate of identification slightly higher than the former, the training time is three times more than the former.
"An automatic speaker recognition system." Chinese University of Hong Kong, 1989. http://library.cuhk.edu.hk/record=b5886206.
Full text"GMM-based speaker recognition for mobile embedded systems." 2004. http://library.cuhk.edu.hk/record=b6073660.
Full text"July 2004."
Thesis (Ph.D.)--Chinese University of Hong Kong, 2004.
Includes bibliographical references (p. 77-81).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Mode of access: World Wide Web.
Abstracts in English and Chinese.
Mohan, Aanchan K. "Combining speech recognition and speaker verification." 2008. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17528.
Full textFang, Yu-Chieh, and 方雨傑. "Using Average Spectrum in English text-independent speaker recognition systems." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/26294843116089040140.
Full text高苑科技大學
電機工程研究所
102
the technology of Voice recognition is more important to speaker identification, because of its technical is practiced and application is extended widely. The recognition system is insufficient whether the research or data to the Taiwanese speakers. so, this paper has represented several common voice recognition technologies to help complete the recognition system. In the paper, mainly in create a speaker identification system which apply to unspecific intonation that spoke by few speakers. The data base which we applied is a private broadcasting station that has system characteristic basic on averaged spectrums, recognition reader is Mahalanobis Distance and Euclidean distance, and judgment basis by minimum range. The research is achieved by the soft-ware Matlab,sampling from 70 people each sample has 60 seconds voice signal. The result under the Mahalanobis Distance has 95% characteristic identification, 89% characteristic identification under the Euclidean distance.
"Text-independent bilingual speaker verification system." 2003. http://library.cuhk.edu.hk/record=b5891732.
Full textThesis (M.Phil.)--Chinese University of Hong Kong, 2003.
Includes bibliographical references (leaves 96-102).
Abstracts in English and Chinese.
Abstract --- p.i
Acknowledgement --- p.iv
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Biometrics --- p.2
Chapter 1.2 --- Speaker Verification --- p.3
Chapter 1.3 --- Overview of Speaker Verification Systems --- p.4
Chapter 1.4 --- Text Dependency --- p.4
Chapter 1.4.1 --- Text-Dependent Speaker Verification --- p.5
Chapter 1.4.2 --- GMM-based Speaker Verification --- p.6
Chapter 1.5 --- Language Dependency --- p.6
Chapter 1.6 --- Normalization Techniques --- p.7
Chapter 1.7 --- Objectives of the Thesis --- p.8
Chapter 1.8 --- Thesis Organization --- p.8
Chapter 2 --- Background --- p.10
Chapter 2.1 --- Background Information --- p.11
Chapter 2.1.1 --- Speech Signal Acquisition --- p.11
Chapter 2.1.2 --- Speech Processing --- p.11
Chapter 2.1.3 --- Engineering Model of Speech Signal --- p.13
Chapter 2.1.4 --- Speaker Information in the Speech Signal --- p.14
Chapter 2.1.5 --- Feature Parameters --- p.15
Chapter 2.1.5.1 --- Mel-Frequency Cepstral Coefficients --- p.16
Chapter 2.1.5.2 --- Linear Predictive Coding Derived Cep- stral Coefficients --- p.18
Chapter 2.1.5.3 --- Energy Measures --- p.20
Chapter 2.1.5.4 --- Derivatives of Cepstral Coefficients --- p.21
Chapter 2.1.6 --- Evaluating Speaker Verification Systems --- p.22
Chapter 2.2 --- Common Techniques --- p.24
Chapter 2.2.1 --- Template Model Matching Methods --- p.25
Chapter 2.2.2 --- Statistical Model Methods --- p.26
Chapter 2.2.2.1 --- HMM Modeling Technique --- p.27
Chapter 2.2.2.2 --- GMM Modeling Techniques --- p.30
Chapter 2.2.2.3 --- Gaussian Mixture Model --- p.31
Chapter 2.2.2.4 --- The Advantages of GMM --- p.32
Chapter 2.2.3 --- Likelihood Scoring --- p.32
Chapter 2.2.4 --- General Approach to Decision Making --- p.35
Chapter 2.2.5 --- Cohort Normalization --- p.35
Chapter 2.2.5.1 --- Probability Score Normalization --- p.36
Chapter 2.2.5.2 --- Cohort Selection --- p.37
Chapter 2.3 --- Chapter Summary --- p.38
Chapter 3 --- Experimental Corpora --- p.39
Chapter 3.1 --- The YOHO Corpus --- p.39
Chapter 3.1.1 --- Design of the YOHO Corpus --- p.39
Chapter 3.1.2 --- Data Collection Process of the YOHO Corpus --- p.40
Chapter 3.1.3 --- Experimentation with the YOHO Corpus --- p.41
Chapter 3.2 --- CUHK Bilingual Speaker Verification Corpus --- p.42
Chapter 3.2.1 --- Design of the CUBS Corpus --- p.42
Chapter 3.2.2 --- Data Collection Process for the CUBS Corpus --- p.44
Chapter 3.3 --- Chapter Summary --- p.46
Chapter 4 --- Text-Dependent Speaker Verification --- p.47
Chapter 4.1 --- Front-End Processing on the YOHO Corpus --- p.48
Chapter 4.2 --- Cohort Normalization Setup --- p.50
Chapter 4.3 --- HMM-based Speaker Verification Experiments --- p.53
Chapter 4.3.1 --- Subword HMM Models --- p.53
Chapter 4.3.2 --- Experimental Results --- p.55
Chapter 4.3.2.1 --- Comparison of Feature Representations --- p.55
Chapter 4.3.2.2 --- Effect of Cohort Normalization --- p.58
Chapter 4.4 --- Experiments on GMM-based Speaker Verification --- p.61
Chapter 4.4.1 --- Experimental Setup --- p.61
Chapter 4.4.2 --- The number of Gaussian Mixture Components --- p.62
Chapter 4.4.3 --- The Effect of Cohort Normalization --- p.64
Chapter 4.4.4 --- Comparison of HMM and GMM --- p.65
Chapter 4.5 --- Comparison with Previous Systems --- p.67
Chapter 4.6 --- Chapter Summary --- p.70
Chapter 5 --- Language- and Text-Independent Speaker Verification --- p.71
Chapter 5.1 --- Front-End Processing of the CUBS --- p.72
Chapter 5.2 --- Language- and Text-Independent Speaker Modeling --- p.73
Chapter 5.3 --- Cohort Normalization --- p.74
Chapter 5.4 --- Experimental Results and Analysis --- p.75
Chapter 5.4.1 --- Number of Gaussian Mixture Components --- p.78
Chapter 5.4.2 --- The Cohort Normalization Effect --- p.79
Chapter 5.4.3 --- Language Dependency --- p.80
Chapter 5.4.4 --- Language-Independency --- p.83
Chapter 5.5 --- Chapter Summary --- p.88
Chapter 6 --- Conclusions and Future Work --- p.90
Chapter 6.1 --- Summary --- p.90
Chapter 6.1.1 --- Feature Comparison --- p.91
Chapter 6.1.2 --- HMM Modeling --- p.91
Chapter 6.1.3 --- GMM Modeling --- p.91
Chapter 6.1.4 --- Cohort Normalization --- p.92
Chapter 6.1.5 --- Language Dependency --- p.92
Chapter 6.2 --- Future Work --- p.93
Chapter 6.2.1 --- Feature Parameters --- p.93
Chapter 6.2.2 --- Model Quality --- p.93
Chapter 6.2.2.1 --- Variance Flooring --- p.93
Chapter 6.2.2.2 --- Silence Detection --- p.94
Chapter 6.2.3 --- Conversational Speaker Verification --- p.95
Bibliography --- p.102
Zhang, Danqing. "Multi-speaker isolated digit recognition using artificial neural networks." Master's thesis, 1994. http://hdl.handle.net/1885/145777.
Full text"Speaker recognition using complementary information from vocal source and vocal tract." Thesis, 2005. http://library.cuhk.edu.hk/record=b6074159.
Full textFor speaker verification, a text-dependent weighting scheme is developed. Analysis results show that the source-tract discrimination ratio varies significantly across different sounds due to the diversity of vocal system configurations in speech production. This thesis analyzes the source-tract speaker discrimination ratio for the 10 Cantonese digits, upon which a digit-dependent source-tract weighting scheme is developed. Information fusion with such digit-dependent weights relatively improves the verification performance by 39.6% in matched conditions.
This thesis investigates the feasibility of using both vocal source and vocal tract information to improve speaker recognition performance. Conventional speaker recognition systems typically employ vocal tract related acoustic features, e.g the Mel-frequency cepstral coefficients (MFCC), for discriminative purpose. Motivated by the physiological significance of the vocal source and vocal tract system in speech production, this thesis develops a speaker recognition system to effectively incorporate these two complementary information sources for improved performance and robustness.
This thesis presents a novel approach of representing the speaker-specific vocal source characteristics. The linear predictive (LP) residual signal is adopted as a good representative of the vocal source excitation, in which the speaker specific information resides on both time and frequency domains. Haar transform and wavelet transform are applied for multi-resolution analyses of the LP residual signal. The resulting vocal source features, namely the Haar octave coefficients of residues (HOCOR) and wavelet octave coefficients of residues (WOCOR), can effectively extract the speaker-specific spectro-temporal characteristics of the LP residual signal. Particularly, with pitch-synchronous wavelet transform, the WOCOR feature set is capable of capturing the pitch-related low frequency properties and the high frequency information associated with pitch epochs, as well as their temporal variations within a pitch period and over consecutive periods. The generated vocal source and vocal tract features are complementary to each other since they are derived from two orthogonal components, the LP residual signal and LP coefficients. Therefore they can be fused to provide better speaker recognition performance. A preliminary scheme of fusing MFCC and WOCOR together illustrated that the identification and verification performance can be respectively improved by 34.6% and 23.6%, both in matched conditions.
To maximize the benefit obtained through the fusion of source and tract information, speaker discrimination dependent fusion techniques have been developed. For speaker identification, a confidence measure, which indicates the reliability of vocal source feature in speaker identification, is derived based on the discrimination ratio between the source and tract features in each identification trial. Information fusion with confidence measure offers better weighted scores given by the two features and avoids possible errors introduced by incorporating source information, thereby improves the identification performance further. Compared with MFCC, relative improvement of 46.8% has been achieved.
Zheng Nengheng.
"November 2005."
Adviser: Pak-Chung Ching.
Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6647.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2005.
Includes bibliographical references (p. 123-135).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstracts in English and Chinese.
School code: 1307.
Chandrasekaran, Aravind. "Efficient methods for rapid UBM training (RUT) for robust speaker verification /." 2008. http://proquest.umi.com/pqdweb?did=1650508671&sid=2&Fmt=2&clientId=10361&RQT=309&VName=PQD.
Full textMoonasar, Viresh. "Hardware implementation of an automatic speaker recognition system using artificial neural networks." Thesis, 2002. http://hdl.handle.net/10321/2714.
Full textThe use of speaker recognition technology in interactive voice response and electronic commerce systems has been limited. This is due to the lack of research attention and published results when compared to all the other areas of speech recognition technologies
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