Improving speaker identification performance under the shouted talking condition using the second-order hidden Markov models

Authors:
Ismail Shahin
Affiliations:
Electrical/Electronics and Computer Engineering Department, University of Sharjah, Sharjah, United Arab Emirates
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2005

Citing 4
Cited 3

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A second-order HMM for high performance word and phoneme-based continuous speech recognition

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Speaker identification in the shouted environment using Suprasegmental Hidden Markov Models

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Employing second-order circular suprasegmental hidden Markov models to enhance speaker identification performance in shouted talking environments

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A trigram hidden Markov model for metadata extraction from heterogeneous references

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Abstract

Speaker identification systems perform well under the neutral talking condition; however, they suffer sharp degradation under the shouted talking condition. In this paper, the second-order hidden Markov models (HMM2s) have been used to improve the recognition performance of isolated-word text-dependent speaker identification systems under the shouted talking condition. Our results show that HMM2s significantly improve the speaker identification performance compared to the first-order hidden Markov models (HMM1s). The average speaker identification performance under the shouted talking condition based on HMM1s is 23%. On the other hand, the average speaker identification performance based on HMM2s is 59%.