Speech Recognition is becoming more important in our daily life. Many applications are starting to use this technology to make them more effective. One important part of the speech recognition process is the training of speech models, which directly affects the performance of the recognition system. The Maximum Likelihood (ML) approach is widely used because of its simplicity and ease of calculation. However, it has some disadvantages. To attain high performance, large number of training utterances is required, which is costly and time consuming to collect. In addition, the objective of the ML approach is not necessarily consistent with the objective of speech recognition, to minimize recognition error, unless the form of the source distri...
The parameters of the standard Hidden Markov Model frame-work for speech recognition are typically t...
Conventional speech recognition systems are based on Gaussian hidden Markov models (HMMs).Discrimina...
The model training algorithm is a critical component in the statistical pattern recognition approach...
Discriminative training has become an important means for estimating model parameters in many statis...
In this work, a framework for efficient discriminative training and modeling is developed and implem...
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...
Discriminative model combination is a new approach in the field of automatic speech recognition, whi...
Abstract—The minimum classification error (MCE) framework for discriminative training is a simple an...
Shigeru Katagiri and various co-authors have (re)introduced a nonstandard error mea-sure which can b...
Discriminative training techniques for Hidden-Markov Models were recently proposed and successfully ...
Speech dynamic feature are routinely used in current speech recognition systems in combination with ...
Discriminative training techniques for Hidden-Markov Models were recently proposed and successfully ...
Model estimation is an important step in pattern recognition tasks. Different optimization criteria,...
The task of an automatic speech recognition system is to convert speech signals into written text by...
The parameters of the standard Hidden Markov Model frame-work for speech recognition are typically t...
Conventional speech recognition systems are based on Gaussian hidden Markov models (HMMs).Discrimina...
The model training algorithm is a critical component in the statistical pattern recognition approach...
Discriminative training has become an important means for estimating model parameters in many statis...
In this work, a framework for efficient discriminative training and modeling is developed and implem...
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...
Discriminative model combination is a new approach in the field of automatic speech recognition, whi...
Abstract—The minimum classification error (MCE) framework for discriminative training is a simple an...
Shigeru Katagiri and various co-authors have (re)introduced a nonstandard error mea-sure which can b...
Discriminative training techniques for Hidden-Markov Models were recently proposed and successfully ...
Speech dynamic feature are routinely used in current speech recognition systems in combination with ...
Discriminative training techniques for Hidden-Markov Models were recently proposed and successfully ...
Model estimation is an important step in pattern recognition tasks. Different optimization criteria,...
The task of an automatic speech recognition system is to convert speech signals into written text by...
The parameters of the standard Hidden Markov Model frame-work for speech recognition are typically t...
Conventional speech recognition systems are based on Gaussian hidden Markov models (HMMs).Discrimina...
The model training algorithm is a critical component in the statistical pattern recognition approach...