Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

speaker independent speech recognition using hidden markov models for persian isolated words

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Speaker-Independent Isolated Word Recognition Using Multiple Hidden Markov Models

A multi-HMM speaker-independent isolated word recognition system is described. In this system , three vector quantization methods, the LBG algorithm, the EM algorithm, and a new MGC algorithm, are used for the classiication of the speech space. These quantizations of the speech space are then used to produce three HMMs for each word in the vocabulary. In the recognition step, the Viterbi algori...

Speaker-independent phone recognition using hidden Markov models

In this paper, we extend hidden Markov modeling to speaker-independent phone recognition. Using multiple codebooks of various LPC parameters and discrete HMMs, we obtain a speakerindependent phone recognition accuracy of 58.8% to 73.8% on the TIMTT database, depending on the type of acoustic and language models used. In comparison, the performance of expert spectrogram readers is only 69% witho...

Improved Hidden Markov Modeling for Speaker-Independent Continuous Speech Recognition

This paper reports recent efforts to further improve the performance of the Sphinx system for speaker-independent continuous speech recognition. The recognition error rate is significantly reduced with incorporation of additional dynamic features, semi-continuous hidden Markov models, and speaker clustering. For the June 1990 (RM2) evaluation test set, the error rates of our current system are ...

Isolated-word speech recognition using hidden Markov models