Talking Condition Identification Using Second-Order Hidden Markov Models

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.

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