754 research outputs found
Master of Science
thesisPresently, speech recognition is gaining worldwide popularity in applications like Google Voice, speech-to-text reporter (speech-to-text transcription, video captioning, real-time transcriptions), hands-free computing, and video games. Research has been done for several years and many speech recognizers have been built. However, most of the speech recognizers fail to recognize the speech accurately. Consider the well-known application of Google Voice, which aids in users search of the web using voice. Though Google Voice does a good job in transcribing the spoken words, it does not accurately recognize the words spoken with different accents. With the fact that several accents are evolving around the world, it is essential to train the speech recognizer to recognize accented speech. Accent classification is defined as the problem of classifying the accents in a given language. This thesis explores various methods to identify the accents. We introduce a new concept of clustering windows of a speech signal and learn a distance metric using specific distance measure over phonetic strings to classify the accents. A language structure is incorporated to learn this distance metric. We also show how kernel approximation algorithms help in learning a distance metric
Physiologically-Motivated Feature Extraction Methods for Speaker Recognition
Speaker recognition has received a great deal of attention from the speech community, and significant gains in robustness and accuracy have been obtained over the past decade. However, the features used for identification are still primarily representations of overall spectral characteristics, and thus the models are primarily phonetic in nature, differentiating speakers based on overall pronunciation patterns. This creates difficulties in terms of the amount of enrollment data and complexity of the models required to cover the phonetic space, especially in tasks such as identification where enrollment and testing data may not have similar phonetic coverage. This dissertation introduces new features based on vocal source characteristics intended to capture physiological information related to the laryngeal excitation energy of a speaker. These features, including RPCC, GLFCC and TPCC, represent the unique characteristics of speech production not represented in current state-of-the-art speaker identification systems. The proposed features are evaluated through three experimental paradigms including cross-lingual speaker identification, cross song-type avian speaker identification and mono-lingual speaker identification. The experimental results show that the proposed features provide information about speaker characteristics that is significantly different in nature from the phonetically-focused information present in traditional spectral features. The incorporation of the proposed glottal source features offers significant overall improvement to the robustness and accuracy of speaker identification tasks
Open-set Speaker Identification
This study is motivated by the growing need for effective extraction of intelligence and evidence from audio recordings in the fight against crime, a need made ever more apparent with the recent expansion of criminal and terrorist organisations. The main focus is to enhance open-set speaker identification process within the speaker identification systems, which are affected by noisy audio data obtained under uncontrolled environments such as in the street, in restaurants or other places of businesses. Consequently, two investigations are initially carried out including the effects of environmental noise on the accuracy of open-set speaker recognition, which thoroughly cover relevant conditions in the considered application areas, such as variable training data length, background noise and real world noise, and the effects of short and varied duration reference data in open-set speaker recognition.
The investigations led to a novel method termed “vowel boosting” to enhance the reliability in speaker identification when operating with varied duration speech data under uncontrolled conditions. Vowels naturally contain more speaker specific information. Therefore, by emphasising this natural phenomenon in speech data, it enables better identification performance. The traditional state-of-the-art GMM-UBMs and i-vectors are used to evaluate “vowel boosting”. The proposed approach boosts the impact of the vowels on the speaker scores, which improves the recognition accuracy for the specific case of open-set identification with short and varied duration of speech material
Text-Independent Speaker Identification using Statistical Learning
The proliferation of voice-activated devices and systems and over-the-phone bank transactions has made our daily affairs much easier in recent times. The ease that these systems offer also call for a need for them to be fail-safe against impersonators. Due to the sensitive information that might be shred on these systems, it is imperative that security be an utmost concern during the development stages. Vital systems like these should incorporate a functionality of discriminating between the actual speaker and impersonators. That functionality is the focus of this thesis.
Several methods have been proposed to be used to achieve this system and some success has been recorded so far. However, due to the vital role this system has to play in securing critical information, efforts have been continually made to reduce the probability of error in the systems. Therefore, statistical learning methods or techniques are utilized in this thesis because they have proven to have high accuracy and efficiency in various other applications. The statistical methods used are Gaussian Mixture Models and Support Vector Machines. These methods have become the de facto techniques for designing speaker identification systems. The effectiveness of the support vector machine is dependent on the type of kernel used. Several kernels have been proposed for achieving better results and we also introduce a kernel in this thesis which will serve as an alternative to the already defined ones. Other factors including the number of components used in modeling the Gaussian Mixture Model (GMM) affect the performance of the system and these factors are used in this thesis and exciting results were obtained
PHONOTACTIC AND ACOUSTIC LANGUAGE RECOGNITION
Práce pojednává o fonotaktickĂ©m a akustickĂ©m pĹ™Ăstupu pro automatickĂ© rozpoznávánĂ jazyka. Prvnà část práce pojednává o fonotaktickĂ©m pĹ™Ăstupu zaloĹľenĂ©m na vĂ˝skytu fonĂ©movĂ˝ch sekvenci v Ĺ™eÄŤi. NejdĹ™Ăve je prezentován popis vĂ˝voje fonĂ©movĂ©ho rozpoznávaÄŤe jako techniky pro pĹ™epis Ĺ™eÄŤi do sekvence smysluplnĂ˝ch symbolĹŻ. HlavnĂ dĹŻraz je kladen na dobrĂ© natrĂ©novánĂ fonĂ©movĂ©ho rozpoznávaÄŤe a kombinaci vĂ˝sledkĹŻ z nÄ›kolika fonĂ©movĂ˝ch rozpoznávaÄŤĹŻ trĂ©novanĂ˝ch na rĹŻznĂ˝ch jazycĂch (ParalelnĂ fonĂ©movĂ© rozpoznávánĂ následovanĂ© jazykovĂ˝mi modely (PPRLM)). Práce takĂ© pojednává o novĂ© technice anti-modely v PPRLM a studuje pouĹľitĂ fonĂ©movĂ˝ch grafĹŻ mĂsto nejlepšĂho pĹ™episu. Na závÄ›r práce jsou porovnány dva pĹ™Ăstupy modelovánĂ vĂ˝stupu fonĂ©movĂ©ho rozpoznávaÄŤe -- standardnĂ n-gramovĂ© jazykovĂ© modely a binárnĂ rozhodovacĂ stromy. HlavnĂ pĹ™Ănos v akustickĂ©m pĹ™Ăstupu je diskriminativnĂ modelovánĂ cĂlovĂ˝ch modelĹŻ jazykĹŻ a prvnĂ experimenty s kombinacĂ diskriminativnĂho trĂ©novánĂ a na pĹ™ĂznacĂch, kde byl odstranÄ›n vliv kanálu. Práce dále zkoumá rĹŻznĂ© druhy technik fĂşzi akustickĂ©ho a fonotaktickĂ©ho pĹ™Ăstupu. Všechny experimenty jsou provedeny na standardnĂch datech z NIST evaluaci konanĂ© v letech 2003, 2005 a 2007, takĹľe jsou pĹ™Ămo porovnatelnĂ© s vĂ˝sledky ostatnĂch skupin zabĂ˝vajĂcĂch se automatickĂ˝m rozpoznávánĂm jazyka. S fĂşzĂ uvedenĂ˝ch technik jsme posunuli state-of-the-art vĂ˝sledky a dosáhli vynikajĂcĂch vĂ˝sledkĹŻ ve dvou NIST evaluacĂch.This thesis deals with phonotactic and acoustic techniques for automatic language recognition (LRE). The first part of the thesis deals with the phonotactic language recognition based on co-occurrences of phone sequences in speech. A thorough study of phone recognition as tokenization technique for LRE is done, with focus on the amounts of training data for phone recognizer and on the combination of phone recognizers trained on several language (Parallel Phone Recognition followed by Language Model - PPRLM). The thesis also deals with novel technique of anti-models in PPRLM and investigates into using phone lattices instead of strings. The work on phonotactic approach is concluded by a comparison of classical n-gram modeling techniques and binary decision trees. The acoustic LRE was addressed too, with the main focus on discriminative techniques for training target language acoustic models and on initial (but successful) experiments with removing channel dependencies. We have also investigated into the fusion of phonotactic and acoustic approaches. All experiments were performed on standard data from NIST 2003, 2005 and 2007 evaluations so that the results are directly comparable to other laboratories in the LRE community. With the above mentioned techniques, the fused systems defined the state-of-the-art in the LRE field and reached excellent results in NIST evaluations.
VOICE BIOMETRICS UNDER MISMATCHED NOISE CONDITIONS
This thesis describes research into effective voice biometrics (speaker recognition) under mismatched noise conditions. Over the last two decades, this class of biometrics has been the subject of considerable research due to its various applications in such areas as telephone banking, remote access control and surveillance. One of the main challenges associated with the deployment of voice biometrics in practice is that of undesired variations in speech characteristics caused by environmental noise. Such variations can in turn lead to a mismatch between the corresponding test and reference material from the same speaker. This is found to adversely affect the performance of speaker recognition in terms of accuracy.
To address the above problem, a novel approach is introduced and investigated. The proposed method is based on minimising the noise mismatch between reference speaker models and the given test utterance, and involves a new form of Test-Normalisation (T-Norm) for further enhancing matching scores under the aforementioned adverse operating conditions. Through experimental investigations, based on the two main classes of speaker recognition (i.e. verification/ open-set identification), it is shown that the proposed approach can significantly improve the performance accuracy under mismatched noise conditions.
In order to further improve the recognition accuracy in severe mismatch conditions, an approach to enhancing the above stated method is proposed. This, which involves providing a closer adjustment of the reference speaker models to the noise condition in the test utterance, is shown to considerably increase the accuracy in extreme cases of noisy test data. Moreover, to tackle the computational burden associated with the use of the enhanced approach with open-set identification, an efficient algorithm for its realisation in this context is introduced and evaluated.
The thesis presents a detailed description of the research undertaken, describes the experimental investigations and provides a thorough analysis of the outcomes
Model-Based Speech Enhancement
Abstract
A method of speech enhancement is developed that reconstructs clean speech from
a set of acoustic features using a harmonic plus noise model of speech. This is a significant
departure from traditional filtering-based methods of speech enhancement.
A major challenge with this approach is to estimate accurately the acoustic features
(voicing, fundamental frequency, spectral envelope and phase) from noisy speech.
This is achieved using maximum a-posteriori (MAP) estimation methods that operate
on the noisy speech. In each case a prior model of the relationship between the
noisy speech features and the estimated acoustic feature is required. These models
are approximated using speaker-independent GMMs of the clean speech features
that are adapted to speaker-dependent models using MAP adaptation and for noise
using the Unscented Transform.
Objective results are presented to optimise the proposed system and a set of subjective
tests compare the approach with traditional enhancement methods. Threeway
listening tests examining signal quality, background noise intrusiveness and
overall quality show the proposed system to be highly robust to noise, performing
significantly better than conventional methods of enhancement in terms of background
noise intrusiveness. However, the proposed method is shown to reduce signal
quality, with overall quality measured to be roughly equivalent to that of the Wiener
filter
Bayesian distance metric learning on i-vector for speaker verification
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 63-66).This thesis explores the use of Bayesian distance metric learning (Bayes_dml) for the task of speaker verification using the i-vector feature representation. We propose a framework that explores the distance constraints between i-vector pairs from the same speaker and different speakers. With an approximation of the distance metric as a weighted covariance matrix of the top eigenvectors from the data covariance matrix, variational inference is used to estimate a posterior distribution of the distance metric. Given speaker labels, we select different-speaker data pairs with the highest cosine scores to form a different-speaker constraint set. This set captures the most discriminative between-speaker variability that exists in the training data. This system is evaluated on the female part of the 2008 NIST SRE dataset. Cosine similarity scoring, as the state-of-the-art approach, is compared to Bayes-dml. Experimental results show the comparable performance between Bayes_dml and cosine similarity scoring. Furthermore, Bayes-dml is insensitive to score normalization, as compared to cosine similarity scoring. Without the requirement of the number of labeled examples, Bayes_dml performs better in the context of limited training databy Xiao Fang.S.M
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