Relevant bibliographies by topics / Speaker recognition / Dissertations / Theses
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Dissertations / Theses on the topic 'Speaker recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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1

Chatzaras, Anargyros, and Georgios Savvidis. "Seamless speaker recognition." Thesis, KTH, Radio Systems Laboratory (RS Lab), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-159021.

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In a technologically advanced society, the average person manages dozens of accounts for e-mail, social networks, e-banking, and other electronic services. As the number of these accounts increases, the need for automatic user identification becomes more essential. Biometrics have long been used to identify people and are the most common (if not the only) method to achieve this task. Over the past few years, smartphones have become frequently used gadgets.  These devices have built-in microphones and are commonly used by a single user or a small set of users, such as a couple or a family. This
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2

VASILAKAKIS, VASILEIOS. "Forensic speaker recognition: speaker and height estimation techniques." Doctoral thesis, Politecnico di Torino, 2014. http://hdl.handle.net/11583/2551370.

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In this work, we analyse some techniques used to perform speaker verification, ex- plaining the steps from feature extraction to mathematical models used for speaker characterisation and discriminative modelling. The main contributions of the au- thor, is a modification on the i–vector generation process, making it either faster or less memory-demanding, a novel way to perform speaker verification by the use of the Pairwise Support Vector Machine and a new way to perform speaker characteri- sation by means of Deep belief networks. Apart from these contributions, additional work in Automatic Sp
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3

Kamarauskas, 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.

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Questions of speaker’s recognition by voice are investigated in this dissertation. Speaker recognition systems, their evolution, problems of recognition, systems of features, questions of speaker modeling and matching used in text-independent and text-dependent speaker recognition are considered too. The text-independent speaker recognition system has been developed during this work. The Gaussian mixture model approach was used for speaker modeling and pattern matching. The automatic method for voice activity detection was proposed. This method is fast and does not require any additional acti
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4

Du, Toit Ilze. "Non-acoustic speaker recognition." Thesis, Stellenbosch : University of Stellenbosch, 2004. http://hdl.handle.net/10019.1/16315.

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Thesis (MScIng)--University of Stellenbosch, 2004.<br>ENGLISH 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
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5

Hong, Z. (Zimeng). "Speaker gender recognition system." Master's thesis, University of Oulu, 2017. http://jultika.oulu.fi/Record/nbnfioulu-201706082645.

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Abstract. Automatic gender recognition through speech is one of the fundamental mechanisms in human-machine interaction. Typical application areas of this technology range from gender-targeted advertising to gender-specific IoT (Internet of Things) applications. It can also be used to narrow down the scope of investigations in crime scenarios. There are many possible methods of recognizing the gender of a speaker. In machine learning applications, the first step is to acquire and convert the natural human voice into a form of machine understandable signal. Useful voice features then could be
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6

Al-Kilani, Menia. "Voice-signature-based Speaker Recognition." University of the Western Cape, 2017. http://hdl.handle.net/11394/5888.

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Magister Scientiae - MSc (Computer Science)<br>Personal identification and the protection of data are important issues because of the ubiquitousness of computing and these have thus become interesting areas of research in the field of computer science. Previously people have used a variety of ways to identify an individual and protect themselves, their property and their information. This they did mostly by means of locks, passwords, smartcards and biometrics. Verifying individuals by using their physical or behavioural fea
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7

Oglesby, J. "Neural models for speaker recognition." Thesis, Swansea University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.638359.

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In recent years a resurgence of interest in neural modeling has taken place. This thesis examines one such class applied to the task of speaker recognition, with direct comparisons made to a contemporary approach based on vector quantisation (VQ). Speaker recognition systems in general, including feature representations and distance measures, are reviewed. The VQ approach, used for comparisons throughout the experimental work, is described in detail. Currently popular neural architectures are also reviewed and associated gradient-based training procedures examined. The performance of a VQ spea
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8

Thompson, J. "Speech variability in speaker recognition." Thesis, Swansea University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.639230.

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This thesis is concerned with investigating the effects of variability on the automatic speaker recognition system performance. Both speaker generated variability and variability of the recording environment are examined. Speaker generated variability (intra-variation) has received less attention than variability of the recording environment, and is therefore the main focus of this thesis. In particular, of most concern is the intra-variation of data typically found in co-operative speaker recognition tasks. That is normally spoken speech, collected over a period of months. To assess the scale
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9

Mukherjee, Rishiraj. "Speaker Recognition Using Shifted MFCC." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4136.

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Speaker Recognition is the art of recognizing a speaker from a given database using speech as the only input. In this thesis we will be discussing a novel approach to detect speakers. Here we will introduce the concept of shifted MFCC to add improvement over the performance from previous work which has shown quite a decent amount of accuracy of about 95% at best. We will be talking about adding different parameters which also contributed in improving the efficiency of speaker recognition. Also we will be testing our algorithm on Text dependent speech data and Text Independent speech data. Our
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10

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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11

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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12

Alkilani, Menia Mohamed. "Voice signature based Speaker Recognition." University of the Western Cape, 2017. http://hdl.handle.net/11394/6196.

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Magister Scientiae - MSc (Computer Science)<br>Personal identification and the protection of data are important issues because of the ubiquitousness of computing and these havethus become interesting areas of research in the field of computer science. Previously people have used a variety of ways to identify an individual and protect themselves, their property and their information.
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13

Mošner, Ladislav. "Microphone Arrays for Speaker Recognition." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363803.

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Tato diplomová práce se zabývá problematikou vzdáleného rozpoznávání mluvčích. V případě dat zachycených odlehlým mikrofonem se přesnost standardního rozpoznávání značně snižuje, proto jsem navrhl dva přístupy pro zlepšení výsledků. Prvním z nich je použití mikrofonního pole (záměrně rozestavené sady mikrofonů), které je schopné nasměrovat virtuální "paprsek" na pozici řečníka. Dále jsem prováděl adaptaci komponent systému (PLDA skórování a extraktoru i-vektorů). S využitím simulace pokojových podmínek jsem syntetizoval trénovací a testovací data ze standardní datové sady NIST 2010. Ukázal jse
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14

CUMANI, SANDRO. "Speaker and Language Recognition Techniques." Doctoral thesis, Politecnico di Torino, 2012. http://hdl.handle.net/11583/2496928.

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In this work we give an overview of different state–of–the–art speaker and language recognition systems. We analyze some techniques to extract and model features from the acoustic signal and to model the speech content by means of phonetic decoding. We then present state–of–the–art generative systems based on latent variable models and discriminative techniques based on Support Vector Machines. We also present the author’s contributions to the field. These contributions cover the different topics presented in this work. First we propose an improvement to Neural Network training for speech deco
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15

Ho, Ka-Lung. "Kernel eigenvoice speaker adaptation /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20HOK.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 56-61). Also available in electronic version. Access restricted to campus users.
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16

Seymour, R. "Audio-visual speech and speaker recognition." Thesis, Queen's University Belfast, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492489.

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In this thesis, a number of important issues relating to the use of both audio and video information for speech and speaker recognition are investigated. A comprehensive comparison of different visual feature types is given, including both geometric and image transformation based features. A new geometric based method for feature extraction is described, as well as the novel use of curvelet based features. Different methods for constructing the feature vectors are compared, as well as feature vector sizes and the use of dynamic features. Each feature type is tested against three types of visua
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17

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.

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This thesis attempts to address the problem of how best to remedy different types of channel distortions on speech when that speech is to be used in automatic speaker recognition and verification systems. Automatic speaker recognition is when a person's voice is analysed by a machine and the person's identity is worked out by the comparison of speech features to a known set of speech features. Automatic speaker verification is when a person claims an identity and the machine determines if that claimed identity is correct or whether that person is an impostor. Channel distortion occurs wh
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18

Domínguez, Sánchez Carlos. "Speaker Recognition in a handheld computer." Thesis, KTH, Kommunikationssystem, CoS, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-99123.

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Handheld computers are widely used, be it a mobile phone, personal digital assistant (PDA), or a media player. Although these devices are personal, often a small set of persons can use a given device, for example a group of friends or a family. The most natural way to communicate for most humans is through speech. Therefore a natural way for these devices to know who is using them is for the device to listen to the user’s speech, i.e., to recognize the speaker based upon their speech. This project exploits the microphone built into most of these devices and asks whether it is possible to devel
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19

Chan, Chit-man, and 陳哲民. "Speaker-independent recognition of Putonghua finals." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1987. http://hub.hku.hk/bib/B12363091.

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(Uncorrected OCR) Abstract of thesis entitled Speaker- Independent Recognition of Putonghua Finals submitted by CHAN, Chit Man for the degree of Doctor of Philosophy at the University of Hong Kong � In December 1987 ABSTRACT A detailed study had been performed to address the problem of speaker-independent recognition of Putonghua (Mandarin) finals. The study included 35 Putonghua finals, 16 of which having trailing nasals. They were spoken by 51 speakers: 38 females, 13 males, in 5 different tones for two times. The sample was spectrally analyzed by a bank of 18 nonoverla
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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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21

Park, Alex S. (Alex Seungryong) 1979. "ASR dependent techniques for speaker recognition." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87287.

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Chan, Chit-man. "Speaker-independent recognition of Putonghua finals /." [Hong Kong : University of Hong Kong], 1987. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12363091.

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23

Vogt, Robert Jeffery. "Automatic speaker recognition under adverse conditions." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/36195/1/Robert_Vogt_Thesis.pdf.

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Speaker verification is the process of verifying the identity of a person by analysing their speech. There are several important applications for automatic speaker verification (ASV) technology including suspect identification, tracking terrorists and detecting a person’s presence at a remote location in the surveillance domain, as well as person authentication for phone banking and credit card transactions in the private sector. Telephones and telephony networks provide a natural medium for these applications. The aim of this work is to improve the usefulness of ASV technology for practical a
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24

Al-Ali, Ahmed Kamil Hasan. "Forensic speaker recognition under adverse conditions." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/130783/1/Ahmed%20Kamil%20Hasan_Al-Ali_Thesis.pdf.

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The performance of forensic speaker recognition systems degrades significantly in the presence of environmental noise and reverberant conditions. This research developed new techniques to improve forensic speaker recognition performance under these conditions using fusion feature extraction techniques and speech enhancement based on the independent component analysis algorithm. A range of forensic speaker recognition applications will benefit from the research outcomes including criminal investigations and law enforcement agencies.
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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.

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This thesis presents methods for supervised and unsupervised speaker adaptation of Gaussian mixture speaker models in text-independent speaker verification. The proposed methods are based on an approach which is able to separate speaker and channel variability so that progressive updating of speaker models can be performed while minimizing the influence of the channel variability associated with the adaptation recordings. This approach relies on a joint factor analysis model of intrinsic speaker variability and session variability where inter-session variation is assumed to result primarily fr
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Thiruvaran, Tharmarajah Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Automatic speaker recognition using phase based features." Awarded by:University of New South Wales. Electrical Engineering & Telecommunications, 2009. http://handle.unsw.edu.au/1959.4/44705.

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Despite recent advances, improving the accuracy of automatic speaker recognition systems remains an important and challenging area of research. This thesis investigates two-phase based features, namely the frequency modulation (FM) feature and the group delay feature in order to improve the speaker recognition accuracy. Introducing features complementary to spectral envelope-based features is a promising approach for increasing the information content of the speaker recognition system. Although phase-based features are motivated by psychophysics and speech production considerations, they have
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Katz, Marcel [Verfasser]. "Discriminative classifiers for speaker Recognition / Marcel Katz." Saarbrücken : Südwestdeutscher Verlag für Hochschulschriften, 2009. http://www.vdm-verlag.de.

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Elvira, Jose M. "Neural networks for speech and speaker recognition." Thesis, Staffordshire University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262314.

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McAuley, J. "Subband correlation and robust speech/speaker recognition." Thesis, Queen's University Belfast, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426761.

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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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Irvine, David Alexander. "A comparison of some speaker recognition techniques." Thesis, University of Ulster, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385661.

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Iliadi, Konstantina. "Bio-inspired voice recognition for speaker identification." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/413949/.

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Speaker identification (SID) aims to identify the underlying speaker(s) given a speech utterance. In a speaker identification system, the first component is the front-end or feature extractor. Feature extraction transforms the raw speech signal into a compact but effective representation that is more stable and discriminative than the original signal. Since the front-end is the first component in the chain, the quality of the later components is strongly determined by its quality. Existing approaches have used several feature extraction methods that have been adopted directly from the speech r
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Fér, Radek. "Speaker Recognition Based on Long Temporal Context." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236121.

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Tato práce se zabývá extrakcí vhodných příznaků pro rozpoznávání řečníka z delších časových úseků. Po představení současných technik pro extrakci takových příznaků navrhujeme a popisujeme novou metodu pracující v časovém rozsahu fonémů a využívající známou techniku i-vektorů. Velké úsilí bylo vynaloženo na nalezení vhodné reprezentace temporálních příznaků, díky kterým by mohly být systémy pro rozpoznávání řečníka robustnější, zejména modelování prosodie. Náš přístup nemodeluje explicitně žádné specifické temporální parametry řeči, namísto toho používá kookurenci řečových rámců jako zdroj temp
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Castellano, Pierre John. "Speaker recognition modelling with artificial neural networks." Thesis, Queensland University of Technology, 1997.

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Ho, Ching-Hsiang. "Speaker modelling for voice conversion." Thesis, Brunel University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365076.

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

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Nosratighods, Mohaddeseh Electrical Engineering &amp 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.

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Identity verification or biometric recognition systems play an important role in our daily lives. Applications include Automatic Teller Machines (ATM), banking and share information retrieval, and personal verification for credit cards. Among the biometric techniques, authentication of speakers by his/her voice is of great importance, since it employs a non-invasive approach and is the only available modality in many applications. However,the performance of Automatic Speaker Verification (ASV) systems degrades significantly under adverse conditions which cause recordings from the same speaker
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38

Wildermoth, 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.

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Speech signal is basically meant to carry the information about the linguistic message. But, it also contains the speaker-specific information. It is generated by acoustically exciting the cavities of the mouth and nose, and can be used to recognize (identify/verify) a person. This thesis deals with the speaker identification task; i.e., to find the identity of a person using his/her speech from a group of persons already enrolled during the training phase. Listeners use many audible cues in identifying speakers. These cues range from high level cues such as semantics and linguistics of the sp
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Wildermoth, Brett Richard. "Text-Independent Speaker Recognition Using Source Based Features." Thesis, Griffith University, 2001. http://hdl.handle.net/10072/366289.

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Speech signal is basically meant to carry the information about the linguistic message. But, it also contains the speaker-specific information. It is generated by acoustically exciting the cavities of the mouth and nose, and can be used to recognize (identify/verify) a person. This thesis deals with the speaker identification task; i.e., to find the identity of a person using his/her speech from a group of persons already enrolled during the training phase. Listeners use many audible cues in identifying speakers. These cues range from high level cues such as semantics and linguistics of the sp
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40

Baker, Brendan J. "Speaker verification incorporating high-level linguistic features." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/17665/1/Brendan_Baker_Thesis.pdf.

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Speaker verification is the process of verifying or disputing the claimed identity of a speaker based on a recorded sample of their speech. Automatic speaker verification technology can be applied to a variety of person authentication and identification applications including forensics, surveillance, national security measures for combating terrorism, credit card and transaction verification, automation and indexing of speakers in audio data, voice based signatures, and over-the-phone security access. The ubiquitous nature of modern telephony systems allows for the easy acquisition and deliver
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Baker, Brendan J. "Speaker verification incorporating high-level linguistic features." Queensland University of Technology, 2008. http://eprints.qut.edu.au/17665/.

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Speaker verification is the process of verifying or disputing the claimed identity of a speaker based on a recorded sample of their speech. Automatic speaker verification technology can be applied to a variety of person authentication and identification applications including forensics, surveillance, national security measures for combating terrorism, credit card and transaction verification, automation and indexing of speakers in audio data, voice based signatures, and over-the-phone security access. The ubiquitous nature of modern telephony systems allows for the easy acquisition and deliver
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42

Tran, Michael. "An approach to a robust speaker recognition system." Diss., This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06062008-164814/.

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Farrús, Cabeceran Mireia. "Fusing prosodic and acoustic information for speaker recognition." Doctoral thesis, Universitat Politècnica de Catalunya, 2008. http://hdl.handle.net/10803/31779.

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El reconeixement automàtic del locutor és la utilització d’una màquina per identificar un individu a partir de d’un missatge parlat. Recentment, aquesta tecnologia ha experimentat un increment en l’ús de diverses aplicacions com el control d’accés, l’autenticació de transaccions, la cooperació amb la justícia, l’analítica forense o la personalització de serveis, entre d’altres. Una de les qüestions centrals que es tracten en aquest camp és el fet de saber quina part del senyal de veu conté informació del locutor. Tradicionalment, els sistemes de reconeixement automàtic del locutor s’han basat
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Khan, Umair. "Self-supervised deep learning approaches to speaker recognition." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/671496.

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In speaker recognition, i-vectors have been the state-of-the-art unsupervised technique over the last few years, whereas x-vectors is becoming the state-of-the-art supervised technique, these days. Recent advances in Deep Learning (DL) approaches to speaker recognition have improved the performance but are constrained to the need of labels for the background data. In practice, labeled background data is not easily accessible, especially when large training data is required. In i-vector based speaker recognition, cosine and Probabilistic Linear Discriminant Analysis (PLDA) are the two basic sco
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Uzuner, Halil. "Robust text-independent speaker recognition over telecommunications systems." Thesis, University of Surrey, 2006. http://epubs.surrey.ac.uk/843391/.

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Biometric recognition methods, using human features such as voice, face or fingeorprints, are increasingly popular for user authentication. Voice is unique in that it is a non-intrusive biometric which can be transmitted over the existing telecommunication networks, thereby allowing remote authentication. Current spealcer recognition systems can provide high recognition rates on clean speech signals. However, their performance has been shown to degrade in real-life applications such as telephone banking, where speech compression and background noise can affect the speech signal. In this work,
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Eriksson, Erik J. "That voice sounds familiar : factors in speaker recognition." Doctoral thesis, Umeå : Department of Philosophy and Linguistics, Umeå University, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1106.

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Falk, Jennie, and Gabriella Hultström. "Support Vector Machines for Optimizing Speaker Recognition Problems." Thesis, KTH, Optimeringslära och systemteori, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-103821.

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Classi cation of data has many applications, amongst others within the eld of speaker recognition. Speaker recognition is the part of speech processing concerned with the task of automatically identifying or verifying speakers using dierent characteristics of their voices. The main focus in speaker recognition is to nd methods that separate data, in order to dierentiate between dierent speakers. In this thesis, such a method is obtained by building a support vector machine, which has proved to be a very good tool for separating all kinds of data. The rst version of the support vector machine i
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Farnes, Karen. "Development of a Speaker Recognition Solution in Vidispine." Thesis, Umeå universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-74180.

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A video database contains an enormous amount of information. In order to search through the database, metadata can be attached to each video. One such type of metadata can be labels containing speakers and where they are speaking. With the help of speaker recognition this type of metadata can automatically be assigned to each video. In this thesis a speaker recognition plug-in for Vidispine, an API media asset management platform, is presented. The plug-in was developed with the help of the LIUM SpkDiarization toolkit for speaker diarization and the ALIZE/LIA RAL toolkit for speaker identifica
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Openshaw, J. P. "The effects of additive noise in speaker recognition." Thesis, Swansea University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.638372.

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This thesis is concerned with <I>text independent speaker recognition</I> and how the performance is affected should additive noise contaminate the speech. Initially, benchmark recognition results are obtained for MFCC, PLP and their Δ derivative features. The drastic effects of noise are clear: recognition errors increase from 3.4% to 60.5% if the test speech has an SNR of 15dB, a level not uncommon as a background office environment. Various attempts at compensating for the adverse effects of noise are investigated, such as explicit modelling, whereby the noise conditions expected to be foun
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50

Cox, S. J. "Techniques for rapid speaker adaptation in speech recognition." Thesis, University of East Anglia, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267271.

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