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Speech/Music Discrimination using Entropy and Dynamism Features in a HMM Classification Framework

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HMM Mixtures (HMM2) for Robust Speech Recognition

Katrin Weber

State-of-the-art automatic speech recognition (ASR) techniques are typically based on hidden Markov models (HMMs) for the modeling of temporal sequences of feature vectors extracted from the speech signal. At the level of each HMM state, Gaussian mixture m ...

IDIAP2003

Spectral Entropy Based Feature for Robust ASR

Hervé Bourlard, Hynek Hermansky, Hemant Misra, Shajith Ikbal

In general, entropy gives us a measure of the number of bits required to represent some information. When applied to probability mass function (PMF), entropy can also be used to measure the ``peakiness'' of a distribution. In this paper, we propose using t ...

IDIAP2003

HMM inference towards flexible speech recognition

One of the difficulties in Automatic Speech Recognizer (ASR) is the pronunciation variability. Each word (modeled by a baseline phonetic transcription in the ASR dictionary) can be pronounced in many different ways depending on many complex qualitative and ...

IDIAP2003

Robust HMM-Based Speech/Music Segmentation

Hervé Bourlard, Jitendra Ajmera

In this paper we present a new approach towards high performance speech/music segmentation on realistic tasks related to the automatic transcription of broadcast news. In the approach presented here, the local probability density function (PDF) estimators ...

2002

Speaker Normalization using HMM2

Hervé Bourlard, Katrin Weber, Shajith Ikbal

In this paper, we present an HMM2 based method for speaker normalization. Introduced as an extension of Hidden Markov Model (HMM), HMM2 differentiates itself from the regular HMM in terms of the emission density modeling, which is done by a set of state-de ...

IDIAP2002

Speaker Normalization using HMM2

Hervé Bourlard, Katrin Weber, Shajith Ikbal

2002

Speech/Music Discrimination using Entropy and Dynamism Features in a HMM Classification Framewor

Hervé Bourlard, Jitendra Ajmera

In this paper, we present a new approach towards high performance speech/music discrimination on realistic tasks related to the automatic transcription of broadcast news. In the approach presented here, the (local) Probability Density Function (PDF) estima ...

IDIAP2001

Robust HMM-Based Speech/Music Segmentation

Hervé Bourlard, Jitendra Ajmera

IDIAP2001

Pronunciation models and their evaluation using confidence measures

Hervé Bourlard

In this report, we present preliminary experiments towards automatic inference and evaluation of pronunciation models based on multiple utterances of each lexicon word and their given baseline pronunciation model (baseform phonetic transcription). In the p ...

IDIAP2001

Audio-Visual Speech Modelling for Continuous Speech Recognition

This paper describes a complete system for audio-visual recognition of continuous speech including robust lip tracking, visual feature extraction, noise-robust acoustic feature extraction, and sensor integration. An appearance based model of the articulato ...

2000