Add to ORKGIn state mixture modelling (SMM), the temporal structure of the observation sequences is represented by the state joint probability distribution where mixtures of states are considered. This technique is considered in an iterative scheme via maximum likelihood estimation. A fuzzy estimation approach is also introduced to cooperate with the SMM model. This new approach not only saves calculations from 2N T T (HMM direct calculation) and N 2T (Forward– backward algorithm) to just only 2NT calculations, but also achieves a better recognition result. Ó 1999 Elsevier Science B.V. All rights reserved
We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixtu...
This paper presents methods to improve the probability density estimation in hidden Markov models fo...
Automatic speech recognition (ASR) systems usually consist of an acoustic model and a language model...
A unified fuzzy approach to statistical models for speech and speaker recognition is presented in th...
We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states shar...
In recent years, under the hidden Markov modeling (HMM) framework, the use of subspace Gaussian mixt...
Abstract—In this paper, a novel mixture linear dynamic model (MLDM) for speech recognition is develo...
EUROSPEECH1997: the 5th European Conference on Speech Communication and Technology , September 22-25...
Abstract { A major limitation of hidden Markov model (HMM)-based automatic speech recognition is the...
Gaussian mixture (GMM)-HMMs, though being the predominant modeling technique for speech recognition,...
A fuzzy clustering based modification of Gaussian mixture models (GMMs) for speaker recognition is p...
In this paper, we propose a state-dependent tied mixture (SDTM) models with variable codebook size t...
This paper shows how a divisive state clustering algorithm that generates acoustic Hidden Markov mod...
In conventional hidden Markov model (HMM) based speech recognisers, the emitting HMM states are mode...
We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixtu...
We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixtu...
This paper presents methods to improve the probability density estimation in hidden Markov models fo...
Automatic speech recognition (ASR) systems usually consist of an acoustic model and a language model...
A unified fuzzy approach to statistical models for speech and speaker recognition is presented in th...
We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states shar...
In recent years, under the hidden Markov modeling (HMM) framework, the use of subspace Gaussian mixt...
Abstract—In this paper, a novel mixture linear dynamic model (MLDM) for speech recognition is develo...
EUROSPEECH1997: the 5th European Conference on Speech Communication and Technology , September 22-25...
Abstract { A major limitation of hidden Markov model (HMM)-based automatic speech recognition is the...
Gaussian mixture (GMM)-HMMs, though being the predominant modeling technique for speech recognition,...
A fuzzy clustering based modification of Gaussian mixture models (GMMs) for speaker recognition is p...
In this paper, we propose a state-dependent tied mixture (SDTM) models with variable codebook size t...
This paper shows how a divisive state clustering algorithm that generates acoustic Hidden Markov mod...
In conventional hidden Markov model (HMM) based speech recognisers, the emitting HMM states are mode...
We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixtu...
We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixtu...
This paper presents methods to improve the probability density estimation in hidden Markov models fo...
Automatic speech recognition (ASR) systems usually consist of an acoustic model and a language model...