Talking Condition Identification Using Second-Order Hidden Markov Models
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2017-07-05 v1
Abstract
This work focuses on enhancing the performance of text-dependent and speaker-dependent talking condition identification systems using second-order hidden Markov models (HMM2s). Our results show that the talking condition identification performance based on HMM2s has been improved significantly compared to first-order hidden Markov models (HMM1s). Our talking conditions in this work are neutral, shouted, loud, angry, happy, and fear.
Keywords
Cite
@article{arxiv.1707.00679,
title = {Talking Condition Identification Using Second-Order Hidden Markov Models},
author = {Ismail Shahin},
journal= {arXiv preprint arXiv:1707.00679},
year = {2017}
}
Comments
3rd International Conference on Information & Communication Technologies: from Theory to Applications, Damascus, Syria, 2008. arXiv admin note: text overlap with arXiv:1706.09691, arXiv:1706.09716