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.
- Publication:
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arXiv e-prints
- Pub Date:
- July 2017
- DOI:
- 10.48550/arXiv.1707.00679
- arXiv:
- arXiv:1707.00679
- Bibcode:
- 2017arXiv170700679S
- Keywords:
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- Computer Science - Sound
- E-Print:
- 3rd International Conference on Information &