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On Variable-Scale Piecewise Stationary Spectral Analysis of Speech Signals for ASR

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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

Robust Speech Recognition and Feature Extraction Using HMM2

Hervé Bourlard, Samy Bengio, Katrin Weber, Shajith Ikbal

This paper presents the theoretical basis and preliminary experimental results of a new HMM model, referred to as HMM2, which can be considered as a mixture of HMMs. In this new model, the emission probabilities of the temporal (primary) HMM are estimated ...

2003

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

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 ...

2003

Automatic Speech Recognition using Dynamic Bayesian Networks with the Energy as an Auxiliary Variable

In current automatic speech recognition (ASR) systems, the energy is not used as part of the feature vector in spite of being a fundamental feature in the speech signal. The noise inherent in its estimation degrades the system performance. In this report w ...

IDIAP2003

On Multi-scale Fourier Transform Analysis of Speech Signals

Hervé Bourlard, Vivek Tyagi

In this paper, we introduce a novel algorithm to perform multi-scale Fourier transform analysis of piecewise stationary signals with application to automatic speech recognition. Such signals are composed of quasi-stationary segments of variable lengths. Th ...

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

Increasing Speech Recognition Noise Robustness with HMM2

Hervé Bourlard, Samy Bengio, Katrin Weber

The purpose of this paper is to investigate the behavior of HMM2 models for the recognition of noisy speech. It has previously been shown that HMM2 is able to model dynamically important structural information inherent in the speech signal, often correspon ...

2002

User-Customized Password HMM Based Speaker Verification

Hervé Bourlard

is presented. The system has no {\it a priori} knowledge of passwords. A hybrid HMM/ANN system is used to infer the phonetic transcription of the password. The emission probabilities are then modeled by a multi-Gaussians HMM model. Evaluation experiments, ...

IDIAP2002

User-Customized Password HMM Based Speaker Verification

Hervé Bourlard

2002

Robust Speech Recognition and Feature Extraction Using HMM2

Hervé Bourlard, Samy Bengio, Katrin Weber, Shajith Ikbal

IDIAP2001