Publications related to A Bayesian Switching Linear Dynamical System for Scale-Invariant robust speech extraction

On Use of Task Independent Training Data in Tandem Feature Extraction

The problem we address in this paper is, whether the feature extraction module trained on large amounts of task independent data, can improve the performance of stochastic models? We show that when there is only a small amount of task specific training dat ...

2004

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 ...

EPFL2003

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 ...

Ecole Polytechnique Federale de Lausanne2003

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

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

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 recognition with auxiliary information

Automatic speech recognition (ASR) is a very challenging problem due to the wide variety of the data that it must be able to deal with. Being the standard tool for ASR, hidden Markov models (HMMs) have proven to work well for ASR when there are controls ov ...

EPFL2003

Speech Recognition with Auxiliary Information

Automatic speech recognition (ASR) is a very challenging problem due to the wide variety of the data that it must be able to deal with. Being the standard tool for ASR, hidden Markov models (HMMs) have proven to work well for ASR when there are controls ov ...

IDIAP2003

Speech Recognition with Auxiliary Information

Automatic speech recognition (ASR) is a very challenging problem due to the wide variety of the data that it must be able to deal with. Being the standard tool for ASR, hidden Markov models (HMMs) have proven to work well for ASR when there are controls ov ...

École Polytechnique Fédérale de Lausanne, Computer Science Department2003

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

A Bayesian Switching Linear Dynamical System for Scale-Invariant robust speech extraction

Graph Chatbot

Chat with Graph Search

On Use of Task Independent Training Data in Tandem Feature Extraction

HMM mixtures (HMM2) for robust speech recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

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

Robust Speech Recognition and Feature Extraction Using HMM2

Speech recognition with auxiliary information

Speech Recognition with Auxiliary Information

Speech Recognition with Auxiliary Information

HMM inference towards flexible speech recognition

Speech Recognition with Auxiliary Information

On Use of Task Independent Training Data in Tandem Feature Extraction

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

HMM mixtures (HMM2) for robust speech recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

Speech recognition with auxiliary information

Speech Recognition with Auxiliary Information

Robust Speech Recognition and Feature Extraction Using HMM2

HMM inference towards flexible speech recognition