Add to ORKGAPSIPA Annual Summit and Conference 2010, December 14-17, 2010, Biopolis, Singapore.This paper presents experimental evaluations of Maximum Mutual Information discriminative training of the acoustic model in a real-environment speech-oriented guidance system "Takemaru-kun"
Hidden Markov model (HMM) has been a popular mathematical approach for sequence classification such...
Discriminative training has become an important means for estimating model parameters in many statis...
This paper presents a new discriminative approach for training Gaussian mixture models (GMMs)of hidd...
Discriminative training techniques for Hidden-Markov Models were recently proposed and successfully ...
Discriminative training techniques for Hidden-Markov Models were recently proposed and successfully ...
Abstract – The paper presents theoretical and experimental issues related with Maximum Mutual Inform...
In this paper, a framework for discriminative training of acoustic models based on Generalised Proba...
In this book, we introduce the background and mainstream methods of probabilistic modeling and discr...
Abstract—Today’s speech recognition systems are based on hidden Markov models (HMMs) with Gaussian m...
The parameters of the standard Hidden Markov Model frame-work for speech recognition are typically t...
Hidden Markov Models (HMMs) are one of the most powerful speech recognition tools available today. E...
AbstractIn this paper, the use of discriminative criteria such as minimum phone error (MPE) and maxi...
Conventional speech recognition systems are based on Gaussian hidden Markov models (HMMs).Discrimina...
This paper describes a lattice-based framework for maximum mutual information estimation (MMIE) of H...
The purpose with this final master degree project was to develop a speech recognition tool, to make ...
Hidden Markov model (HMM) has been a popular mathematical approach for sequence classification such...
Discriminative training has become an important means for estimating model parameters in many statis...
This paper presents a new discriminative approach for training Gaussian mixture models (GMMs)of hidd...
Discriminative training techniques for Hidden-Markov Models were recently proposed and successfully ...
Discriminative training techniques for Hidden-Markov Models were recently proposed and successfully ...
Abstract – The paper presents theoretical and experimental issues related with Maximum Mutual Inform...
In this paper, a framework for discriminative training of acoustic models based on Generalised Proba...
In this book, we introduce the background and mainstream methods of probabilistic modeling and discr...
Abstract—Today’s speech recognition systems are based on hidden Markov models (HMMs) with Gaussian m...
The parameters of the standard Hidden Markov Model frame-work for speech recognition are typically t...
Hidden Markov Models (HMMs) are one of the most powerful speech recognition tools available today. E...
AbstractIn this paper, the use of discriminative criteria such as minimum phone error (MPE) and maxi...
Conventional speech recognition systems are based on Gaussian hidden Markov models (HMMs).Discrimina...
This paper describes a lattice-based framework for maximum mutual information estimation (MMIE) of H...
The purpose with this final master degree project was to develop a speech recognition tool, to make ...
Hidden Markov model (HMM) has been a popular mathematical approach for sequence classification such...
Discriminative training has become an important means for estimating model parameters in many statis...
This paper presents a new discriminative approach for training Gaussian mixture models (GMMs)of hidd...