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User-Customized Password Speaker Verification based on HMM/ANN and GMM Models

Publications associées (69)

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EMPLOYMENT OF SUBSPACE GAUSSIAN MIXTURE MODELS IN SPEAKER RECOGNITION

Petr Motlicek, Subhadeep Dey

This paper presents Subspace Gaussian Mixture Model (SGMM) approach employed as a probabilistic generative model to estimate speaker vector representations to be subsequently used in the speaker verification task. SGMMs have already been shown to significa ...

Idiap2015

KL-HMM and Probabilistic Lexical Modeling

Ramya Rasipuram

Kullback-Leibler divergence based hidden Markov model (KL-HMM) is an approach where a posteriori probabilities of phonemes estimated by artificial neural networks (ANN) are modeled directly as feature observation. In this paper, we show the relation betwee ...

Idiap2013

ACCENT ADAPTATION USING SUBSPACE GAUSSIAN MIXTURE MODELS

Petr Motlicek, Philip Neil Garner

This paper investigates employment of Subspace Gaussian Mixture Models (SGMMs) for acoustic model adaptation towards different accents for English speech recognition. The SGMMs comprise globally-shared and state-specific parameters which can efficiently be ...

2013

ACCENT ADAPTATION USING SUBSPACE GAUSSIAN MIXTURE MODELS

Petr Motlicek, Philip Neil Garner

Idiap2013

Using KL-divergence and multilingual information to improve ASR for under-resourced languages

Hervé Bourlard, Philip Neil Garner, David Imseng

Setting out from the point of view that automatic speech recognition (ASR) ought to benefit from data in languages other than the target language, we propose a novel Kullback-Leibler (KL) divergence based method that is able to exploit multilingual informa ...

2012

Improving non-native ASR through stochastic multilingual phoneme space transformations

Hervé Bourlard, Philip Neil Garner, John David Scott Dines, David Imseng

We propose a stochastic phoneme space transformation technique that allows the conversion of conditional source phoneme posterior probabilities (conditioned on the acoustics) into target phoneme posterior probabilities. The source and target phonemes can b ...

2011

Improving non-native ASR through stochastic multilingual phoneme space transformations

Hervé Bourlard, Philip Neil Garner, John David Scott Dines, David Imseng

Idiap2011

Enhanced Phone Posteriors for Improving Speech Recognition Systems

Hervé Bourlard, Hamed Ketabdar

Using phone posterior probabilities has been increasingly explored for improving automatic speech recognition (ASR) systems. In this paper, we propose two approaches for hierarchically enhancing these phone posteriors, by integrating long acoustic context, ...

2010

Neural conditional random fields

We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Markov networks, yielding a powerful and scalable probabilistic model that we ap ...

JMLR: W&CP2010

The EMIME European project is conducting research in the development of technologies for mobile, personalised speech-tospeech translation systems. The hidden Markov model is being used as the underlying technology in both automatic speech recognition (ASR) ...

2009