  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.
