55 research outputs found
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Dialect Recognition Using a Phone-GMM-Supervector-Based SVM Kernel
In this paper, we introduce a new approach to dialect recognition which relies on the hypothesis that certain phones are realized differently across dialects. Given a speaker’s utterance, we first obtain the most likely phone sequence using a phone recognizer. We then extract GMM Supervectors for each phone instance. Using these vectors, we design a kernel function that computes the similarities of phones between pairs of utterances. We employ this kernel to train SVM classifiers that estimate posterior probabilities, used during recognition. Testing our approach on four Arabic dialects from 30s cuts, we compare our performance to five approaches: PRLM; GMM-UBM; our own improved version of GMM-UBM which employs fMLLR adaptation; our recent discriminative phonotactic approach; and a state-of-the-art system: SDC-based GMM-UBM discriminatively trained. Our kernel-based technique outperforms all these previous approaches; the overall EER of our system is 4.9%
Acoustic Approaches to Gender and Accent Identification
There has been considerable research on the problems of speaker and language recognition
from samples of speech. A less researched problem is that of accent recognition. Although this
is a similar problem to language identification, di�erent accents of a language exhibit more
fine-grained di�erences between classes than languages. This presents a tougher problem
for traditional classification techniques. In this thesis, we propose and evaluate a number of
techniques for gender and accent classification. These techniques are novel modifications and
extensions to state of the art algorithms, and they result in enhanced performance on gender
and accent recognition.
The first part of the thesis focuses on the problem of gender identification, and presents a
technique that gives improved performance in situations where training and test conditions are
mismatched.
The bulk of this thesis is concerned with the application of the i-Vector technique to accent
identification, which is the most successful approach to acoustic classification to have emerged
in recent years. We show that it is possible to achieve high accuracy accent identification without
reliance on transcriptions and without utilising phoneme recognition algorithms. The thesis
describes various stages in the development of i-Vector based accent classification that improve
the standard approaches usually applied for speaker or language identification, which are
insu�cient. We demonstrate that very good accent identification performance is possible with
acoustic methods by considering di�erent i-Vector projections, frontend parameters, i-Vector
configuration parameters, and an optimised fusion of the resulting i-Vector classifiers we can
obtain from the same data.
We claim to have achieved the best accent identification performance on the test corpus
for acoustic methods, with up to 90% identification rate. This performance is even better than
previously reported acoustic-phonotactic based systems on the same corpus, and is very close
to performance obtained via transcription based accent identification. Finally, we demonstrate
that the utilization of our techniques for speech recognition purposes leads to considerably
lower word error rates.
Keywords: Accent Identification, Gender Identification, Speaker Identification, Gaussian
Mixture Model, Support Vector Machine, i-Vector, Factor Analysis, Feature Extraction, British
English, Prosody, Speech Recognition
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Automatic Dialect and Accent Recognition and its Application to Speech Recognition
A fundamental challenge for current research on speech science and technology is understanding and modeling individual variation in spoken language. Individuals have their own speaking styles, depending on many factors, such as their dialect and accent as well as their socioeconomic background. These individual differences typically introduce modeling difficulties for large-scale speaker-independent systems designed to process input from any variant of a given language. This dissertation focuses on automatically identifying the dialect or accent of a speaker given a sample of their speech, and demonstrates how such a technology can be employed to improve Automatic Speech Recognition (ASR). In this thesis, we describe a variety of approaches that make use of multiple streams of information in the acoustic signal to build a system that recognizes the regional dialect and accent of a speaker. In particular, we examine frame-based acoustic, phonetic, and phonotactic features, as well as high-level prosodic features, comparing generative and discriminative modeling techniques. We first analyze the effectiveness of approaches to language identification that have been successfully employed by that community, applying them here to dialect identification. We next show how we can improve upon these techniques. Finally, we introduce several novel modeling approaches -- Discriminative Phonotactics and kernel-based methods. We test our best performing approach on four broad Arabic dialects, ten Arabic sub-dialects, American English vs. Indian English accents, American English Southern vs. Non-Southern, American dialects at the state level plus Canada, and three Portuguese dialects. Our experiments demonstrate that our novel approach, which relies on the hypothesis that certain phones are realized differently across dialects, achieves new state-of-the-art performance on most dialect recognition tasks. This approach achieves an Equal Error Rate (EER) of 4% for four broad Arabic dialects, an EER of 6.3% for American vs. Indian English accents, 14.6% for American English Southern vs. Non-Southern dialects, and 7.9% for three Portuguese dialects. Our framework can also be used to automatically extract linguistic knowledge, specifically the context-dependent phonetic cues that may distinguish one dialect form another. We illustrate the efficacy of our approach by demonstrating the correlation of our results with geographical proximity of the various dialects. As a final measure of the utility of our studies, we also show that, it is possible to improve ASR. Employing our dialect identification system prior to ASR to identify the Levantine Arabic dialect in mixed speech of a variety of dialects allows us to optimize the engine's language model and use Levantine-specific acoustic models where appropriate. This procedure improves the Word Error Rate (WER) for Levantine by 4.6% absolute; 9.3% relative. In addition, we demonstrate in this thesis that, using a linguistically-motivated pronunciation modeling approach, we can improve the WER of a state-of-the art ASR system by 2.2% absolute and 11.5% relative WER on Modern Standard Arabic
Bayesian distance metric learning and its application in automatic speaker recognition systems
This paper proposes state-of the-art Automatic Speaker Recognition System (ASR) based on Bayesian Distance Learning Metric as a feature extractor. In this modeling, I explored the constraints of the distance between modified and simplified i-vector pairs by the same speaker and different speakers. An approximation of the distance metric is used as a weighted covariance matrix from the higher eigenvectors of the covariance matrix, which is used to estimate the posterior distribution of the metric distance. Given a speaker tag, I select the data pair of the different speakers with the highest cosine score to form a set of speaker constraints. This collection captures the most discriminating variability between the speakers in the training data. This Bayesian distance learning approach achieves better performance than the most advanced methods. Furthermore, this method is insensitive to normalization compared to cosine scores. This method is very effective in the case of limited training data. The modified supervised i-vector based ASR system is evaluated on the NIST SRE 2008 database. The best performance of the combined cosine score EER 1.767% obtained using LDA200 + NCA200 + LDA200, and the best performance of Bayes_dml EER 1.775% obtained using LDA200 + NCA200 + LDA100. Bayesian_dml overcomes the combined norm of cosine scores and is the best result of the short2-short3 condition report for NIST SRE 2008 data
An investigation of supervector regression for forensic voice comparison on small data
International audienceThe present paper deals with an observer design for a nonlinear lateral vehicle model. The nonlinear model is represented by an exact Takagi-Sugeno (TS) model via the sector nonlinearity transformation. A proportional multiple integral observer (PMIO) based on the TS model is designed to estimate simultaneously the state vector and the unknown input (road curvature). The convergence conditions of the estimation error are expressed under LMI formulation using the Lyapunov theory which guaranties bounded error. Simulations are carried out and experimental results are provided to illustrate the proposed observer
Local representations and random sampling for speaker verification
In text-independent speaker verification, studies focused on compensating intra-speaker variabilities at the modeling stage through the last decade. Intra-speaker variabilities may be due to channel effects, phonetic content or the speaker himself in the form of speaking style, emotional state, health or other similar factors. Joint Factor Analysis, Total Variability Space compensation, Nuisance Attribute Projection are some of the most successful approaches for inter-session variability compensation in the literature. In this thesis, we criticize the assumptions of low dimensionality of channel space in these methods and propose to partition the acoustic space into local regions. Intra-speaker variability compensation may be done in each local space separately. Two architectures are proposed depending on whether the subsequent modeling and scoring steps will also be done locally or globally. We have also focused on a particular component of intra-speaker variability, namely within-session variability. The main source of within-session variability is the differences in the phonetic content of speech segments in a single utterance. The variabilities in phonetic content may be either due to across acoustic event variabilities or due to differences in the actual realizations of the acoustic events. We propose a method to combat these variabilities through random sampling of training utterance. The method is shown to be effective both in short and long test utterances
Automatic speaker recognition
06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır
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
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