Talking Condition Identification Using Second-Order Hidden Markov Models

07/01/2017 ∙ by Ismail Shahin, et al. ∙ 0

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.

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