324 research outputs found
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification
There are a number of studies about extraction of bottleneck (BN) features
from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases
and triphone states for improving the performance of text-dependent speaker
verification (TD-SV). However, a moderate success has been achieved. A recent
study [1] presented a time contrastive learning (TCL) concept to explore the
non-stationarity of brain signals for classification of brain states. Speech
signals have similar non-stationarity property, and TCL further has the
advantage of having no need for labeled data. We therefore present a TCL based
BN feature extraction method. The method uniformly partitions each speech
utterance in a training dataset into a predefined number of multi-frame
segments. Each segment in an utterance corresponds to one class, and class
labels are shared across utterances. DNNs are then trained to discriminate all
speech frames among the classes to exploit the temporal structure of speech. In
addition, we propose a segment-based unsupervised clustering algorithm to
re-assign class labels to the segments. TD-SV experiments were conducted on the
RedDots challenge database. The TCL-DNNs were trained using speech data of
fixed pass-phrases that were excluded from the TD-SV evaluation set, so the
learned features can be considered phrase-independent. We compare the
performance of the proposed TCL bottleneck (BN) feature with those of
short-time cepstral features and BN features extracted from DNNs discriminating
speakers, pass-phrases, speaker+pass-phrase, as well as monophones whose labels
and boundaries are generated by three different automatic speech recognition
(ASR) systems. Experimental results show that the proposed TCL-BN outperforms
cepstral features and speaker+pass-phrase discriminant BN features, and its
performance is on par with those of ASR derived BN features. Moreover,....Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other work
Robust text independent closed set speaker identification systems and their evaluation
PhD ThesisThis thesis focuses upon text independent closed set speaker
identi cation. The contributions relate to evaluation studies in the
presence of various types of noise and handset e ects. Extensive
evaluations are performed on four databases.
The rst contribution is in the context of the use of the Gaussian
Mixture Model-Universal Background Model (GMM-UBM) with
original speech recordings from only the TIMIT database. Four main
simulations for Speaker Identi cation Accuracy (SIA) are presented
including di erent fusion strategies: Late fusion (score based), early
fusion (feature based) and early-late fusion (combination of feature and
score based), late fusion using concatenated static and dynamic
features (features with temporal derivatives such as rst order
derivative delta and second order derivative delta-delta features,
namely acceleration features), and nally fusion of statistically
independent normalized scores.
The second contribution is again based on the GMM-UBM
approach. Comprehensive evaluations of the e ect of Additive White
Gaussian Noise (AWGN), and Non-Stationary Noise (NSN) (with and
without a G.712 type handset) upon identi cation performance are
undertaken. In particular, three NSN types with varying Signal to
Noise Ratios (SNRs) were tested corresponding to: street tra c, a bus
interior and a crowded talking environment. The performance
evaluation also considered the e ect of late fusion techniques based on
score fusion, namely mean, maximum, and linear weighted sum fusion.
The databases employed were: TIMIT, SITW, and NIST 2008; and 120
speakers were selected from each database to yield 3,600 speech
utterances.
The third contribution is based on the use of the I-vector, four
combinations of I-vectors with 100 and 200 dimensions were employed.
Then, various fusion techniques using maximum, mean, weighted sum
and cumulative fusion with the same I-vector dimension were used to
improve the SIA. Similarly, both interleaving and concatenated I-vector
fusion were exploited to produce 200 and 400 I-vector dimensions. The
system was evaluated with four di erent databases using 120 speakers
from each database. TIMIT, SITW and NIST 2008 databases were
evaluated for various types of NSN namely, street-tra c NSN,
bus-interior NSN and crowd talking NSN; and the G.712 type handset
at 16 kHz was also applied.
As recommendations from the study in terms of the GMM-UBM
approach, mean fusion is found to yield overall best performance in terms
of the SIA with noisy speech, whereas linear weighted sum fusion is
overall best for original database recordings. However, in the I-vector
approach the best SIA was obtained from the weighted sum and the
concatenated fusion.Ministry of Higher Education
and Scienti c Research (MoHESR), and the Iraqi Cultural Attach e,
Al-Mustansiriya University, Al-Mustansiriya University College of
Engineering in Iraq for supporting my PhD scholarship
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
Intersession Variability Compensation in Language and Speaker Identification
Variabilita kanálu a hovoru je velmi důležitým problémem v úloze rozpoznávání mluvčího. V současné době je ve velkém množství vědeckých článků uvedeno několik technik pro kompenzaci vlivu kanálu. Kompenzace vlivu kanálu může být implementována jak v doméně modelu, tak i v doménách příznaků i skóre. Relativně nová výkoná technika je takzvaná eigenchannel adaptace pro GMM (Gaussian Mixture Models). Mevýhodou této metody je nemožnost její aplikace na jiné klasifikátory, jako napřílad takzvané SVM (Support Vector Machines), GMM s různým počtem Gausových komponent nebo v rozpoznávání řeči s použitím skrytých markovových modelů (HMM). Řešením může být aproximace této metody, eigenchannel adaptace v doméně příznaků. Obě tyto techniky, eigenchannel adaptace v doméně modelu a doméně příznaků v systémech rozpoznávání mluvčího, jsou uvedeny v této práci. Po dosažení dobrých výsledků v rozpoznávání mluvčího, byl přínos těchto technik zkoumán pro akustický systém rozpoznávání jazyka zahrnující 14 jazyků. V této úloze má nežádoucí vliv nejen variabilita kanálu, ale i variabilita mluvčího. Výsledky jsou prezentovány na datech definovaných pro evaluaci rozpoznávání mluvčího z roku 2006 a evaluaci rozpoznávání jazyka v roce 2007, obě organizované Amerických Národním Institutem pro Standard a Technologie (NIST)Varibiality in the channel and session is an important issue in the text-independent speaker recognition task. To date, several techniques providing channel and session variability compensation were introduced in a number of scientic papers. Such implementation can be done in feature, model and score domain. Relatively new and powerful approach to remove channel distortion is so-called eigenchannel adaptation for Gaussian Mixture Models (GMM). The drawback of the technique is that it is not applicable in its original implementation to different types of classifiers, eg. Support Vector Machines (SVM), GMM with different number of Gaussians or in speech recognition task using Hidden Markov Models (HMM). The solution can be the approximation of the technique, eigenchannel adaptation in feature domain. Both, the original eigenchannel adaptation and eigenchannel adaptation on features in task of speaker recognition are presented. After achieving good results in speaker recognition, contribution of the same techniques was examined in acoustic language identification system with languages. In this task undesired factors are channel and speaker variability. Presented results are presented on the NIST Speaker Recognition Evaluation 2006 data and NIST Language Recognition Evaluation 2007 data.
Glottal Source Cepstrum Coefficients Applied to NIST SRE 2010
Through the present paper, a novel feature set for speaker recognition based on glottal estimate information is presented. An iterative algorithm is used to derive the vocal tract and glottal source estimations from speech signal. In order to test the importance of glottal source information in speaker characterization, the novel feature set has been tested in the 2010 NIST Speaker Recognition Evaluation (NIST SRE10). The proposed system uses glottal estimate parameter templates and classical cepstral information to build a model for each speaker involved in the recognition process. ALIZE [1] open-source software has been used to create the GMM models for both background and target speakers. Compared to using mel-frequency cepstrum coefficients (MFCC), the misclassification rate for the NIST SRE 2010 reduced from 29.43% to 27.15% when glottal source features are use
Compensation of Nuisance Factors for Speaker and Language Recognition
The variability of the channel and environment is
one of the most important factors affecting the performance of
text-independent speaker verification systems. The best techniques
for channel compensation are model based. Most of them have
been proposed for Gaussian mixture models, while in the feature
domain blind channel compensation is usually performed. The
aim of this work is to explore techniques that allow more accurate
intersession compensation in the feature domain. Compensating
the features rather than the models has the advantage that the
transformed parameters can be used with models of a different
nature and complexity and for different tasks. In this paper,
we evaluate the effects of the compensation of the intersession
variability obtained by means of the channel factors approach. In
particular, we compare channel variability modeling in the usual
Gaussian mixture model domain, and our proposed feature domain
compensation technique. We show that the two approaches
lead to similar results on the NIST 2005 Speaker Recognition
Evaluation data with a reduced computation cost. We also report
the results of a system, based on the intersession compensation
technique in the feature space that was among the best participants
in the NIST 2006 Speaker Recognition Evaluation. Moreover, we
show how we obtained significant performance improvement in
language recognition by estimating and compensating, in the
feature domain, the distortions due to interspeaker variability
within the same language.
Index Terms—Factor anal
- …