Topic Identification for Speech without ASR

Modern topic identification (topic ID) systems for speech use automatic speech recognition (ASR) to produce speech transcripts, and perform supervised classification on such ASR outputs. However, under resource-limited conditions, the manually transcribed speech required to develop standard ASR systems can be severely limited or unavailable. In this paper, we investigate alternative unsupervised solutions to obtaining tokenizations of speech in terms of a vocabulary of automatically discovered word-like or phoneme-like units, without depending on the supervised training of ASR systems. Moreover, using automatic phoneme-like tokenizations, we demonstrate that a convolutional neural network based framework for learning spoken document representations provides competitive performance compared to a standard bag-of-words representation, as evidenced by comprehensive topic ID evaluations on both single-label and multi-label classification tasks.Comment: 5 pages, 2 figures; accepted for publication at Interspeech 201

An Empirical Evaluation of Zero Resource Acoustic Unit Discovery

Acoustic unit discovery (AUD) is a process of automatically identifying a categorical acoustic unit inventory from speech and producing corresponding acoustic unit tokenizations. AUD provides an important avenue for unsupervised acoustic model training in a zero resource setting where expert-provided linguistic knowledge and transcribed speech are unavailable. Therefore, to further facilitate zero-resource AUD process, in this paper, we demonstrate acoustic feature representations can be significantly improved by (i) performing linear discriminant analysis (LDA) in an unsupervised self-trained fashion, and (ii) leveraging resources of other languages through building a multilingual bottleneck (BN) feature extractor to give effective cross-lingual generalization. Moreover, we perform comprehensive evaluations of AUD efficacy on multiple downstream speech applications, and their correlated performance suggests that AUD evaluations are feasible using different alternative language resources when only a subset of these evaluation resources can be available in typical zero resource applications.Comment: 5 pages, 1 figure; Accepted for publication at ICASSP 201

ATVS-UAM NIST LRE 2009 System Description

Official contribution of the National Institute of Standards and Technology; not subject to copyright in the United States.ATVS-UAM submits a fast, light and efficient single system. The use of a task-adapted nonspeech-recognition-based VAD (apart from NIST conversation labels) and gender-dependent total variability compensation technology allows our submitted system to obtain excellent development results with SRE08 data with exceptional computational efficiency. In order to test the VAD influence in the evaluation results, a contrastive equivalent system has been submitted exclusively changing ATVS VAD labels with BUT publicly contributed ones. In all contributed systems, two gender-independent calibrations have been trained with respectively telephone-only and mic (either mic-tel, tel-mic or mic-mic) data. The submitted systems have been designed for English speech in an application-independent way, all results being interpretable in the form of calibrated likelihood ratios to be properly evaluated with Cllr. Sample development results with English SRE08 data are 0.53% (male) and 1.11% (female) EER in tel-tel data (optimistic as all English speakers in SRE08 are included in total variability matrices), going up to 3.5% (tel-tel) to 5.1% EER (tel-mic) in pessimistic cross-validation experiments (25% of test speakers totally excluded from development data in each xval set). The submitted system is extremely light in computational resources, running 77 times faster than real time. Moreover, once VAD and feature extraction are performed (the heaviest components of our system), training and testing are performed respectively at 5300 and 2950 times faster than real time

On the use of high-level information in speaker and language recognition

Actas de las IV Jornadas de Tecnología del Habla (JTH 2006)Automatic Speaker Recognition systems have been largely dominated by acoustic-spectral based systems, relying in proper modelling of the short-term vocal tract of speakers. However, there is scientific and intuitive evidence that speaker specific information is embedded in the speech signal in multiple short- and long-term characteristics. In this work, a multilevel speaker recognition system combining acoustic, phonotactic and prosodic subsystems is presented and assessed using NIST 2005 Speaker Recognition Evaluation data. For language recognition systems, the NIST 2005 Language Recognition Evaluation was selected to measure performance of a high-level language recognition systems

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

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 $14$ 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.

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