Add to ORKGIn this paper we develop automatic speech segmentation to phonemes using hybrid system based on Hidden Markov Model (HMM) and Artificial Neutral Network (ANN). It was shown that usage of ANN in order to estimate local probability in HMM leads to optimal global probability estimation in the general case, without imposition of additional model conditions. The result of automatic segmentation is close to the manual one, and can be successfully used in real applications for speech data segmentation and speech recognition system training
AbstractThis paper describes the development of a context independent, small vocabulary, connectioni...
This paper describes a hybrid system for continuous speech recognition consisting of a neural networ...
Natural language processing enables computer and machines to understand and speak human languages. S...
In this paper we develop automatic speech segmentation to phonemes using hybrid system based on Hid...
In spite of the advances accomplished throughout the last decades, automatic speech recognition (ASR...
Acoustic modeling in state-of-the-art speech recognition systems usually relies on hidden Markov mod...
A system for automatic labeling and segmentation of speech signals starting from their corresponding...
In spite of the advances accomplished throughout the last decades by a number of research teams, Aut...
Although Automatic Speech Recognition (ASR) systems based on hidden Markov models (HMMs) are popular...
In this paper, after an a review of the previous work done in this field, the most frequently used a...
In this paper, we briefly describe REMAP, an approach for the training and estimation of posterior p...
Automatic Speech Recognition (ASR) is a challenging classification task over sequences of acoustic f...
A summary of the theory of the hybrid connectionist HMM (hidden Markov model) continuous speech reco...
International audienceThis paper introduces a new approach for the automatic segmentation of corpora...
This paper describes the refinement of the automatic speech segmentation into phones obtained via Hi...
AbstractThis paper describes the development of a context independent, small vocabulary, connectioni...
This paper describes a hybrid system for continuous speech recognition consisting of a neural networ...
Natural language processing enables computer and machines to understand and speak human languages. S...
In this paper we develop automatic speech segmentation to phonemes using hybrid system based on Hid...
In spite of the advances accomplished throughout the last decades, automatic speech recognition (ASR...
Acoustic modeling in state-of-the-art speech recognition systems usually relies on hidden Markov mod...
A system for automatic labeling and segmentation of speech signals starting from their corresponding...
In spite of the advances accomplished throughout the last decades by a number of research teams, Aut...
Although Automatic Speech Recognition (ASR) systems based on hidden Markov models (HMMs) are popular...
In this paper, after an a review of the previous work done in this field, the most frequently used a...
In this paper, we briefly describe REMAP, an approach for the training and estimation of posterior p...
Automatic Speech Recognition (ASR) is a challenging classification task over sequences of acoustic f...
A summary of the theory of the hybrid connectionist HMM (hidden Markov model) continuous speech reco...
International audienceThis paper introduces a new approach for the automatic segmentation of corpora...
This paper describes the refinement of the automatic speech segmentation into phones obtained via Hi...
AbstractThis paper describes the development of a context independent, small vocabulary, connectioni...
This paper describes a hybrid system for continuous speech recognition consisting of a neural networ...
Natural language processing enables computer and machines to understand and speak human languages. S...