The authors describe an HMM (hidden Markov model) clustering procedure and discuss its application to connected-word systems and to large-vocabulary ...

This paper describes such an HMM clustering procedure and discusses its application to connected word systems and to large vocabulary recognition based on ...

The authors describe an HMM (hidden Markov model) clustering procedure and discuss its application to connected-word systems and to large-vocabulary ...

The authors describe an HMM (hidden Markov model) clustering procedure and discuss its application to connected-word systems and to large-vocabulary ...

It has been shown that when there are a sufficiently large number of training tokens of a given speech recognition unit (e.g., words, phones, syllables, ...

Nov 14, 2013 · It has been shown that when there are a sufficiently large number of training tokens of a given speech recognition unit (e.g., words, ...

Hidden Markov Models (HMMs) provide a simple and effective frame- work for modelling time-varying spectral vector sequences. As a con- sequence, almost all ...

Sep 8, 2019 · To learn a GMM, say for a 2-component GMM, we feed features extracted from the training data to fit the parameters of these two clusters.

This paper discusses a probabilistic model-based approach to clus- tering sequences, using hidden Markov models (HMMs). The prob-.

models) using a Viterbi algorithm. This matching occurs at each frame of the input signal, thereby generating a sequence of best matches for each frame.