ISCA Archive Interspeech 2013
ISCA Archive Interspeech 2013

Evaluation of fundamental validity in applying AR-HMM with automatic topology generation to pathology voice analysis

Akira Sasou

Voice-pathology detection from a subject's voice is a promising technology for the pre-diagnosis of larynx diseases. Glottal source estimation in particular plays a very important role in voice-pathology analysis. To more accurately estimate the spectral envelope and glottal source of the pathology voice, we propose a method that can automatically generate the topology of the Glottal Source Hidden Markov Model (GS-HMM), as well as estimate the Auto-Regressive (AR)-HMM parameter by combining the AR-HMM parameter estimation method and the Minimum Description Length-based Successive State Splitting (MDL-SSS) algorithm. This paper evaluates the fundamental validity of pathology-voice analysis based on the proposed method. The experiment results confirmed the feasibility and fundamental validity of the proposed method.


doi: 10.21437/Interspeech.2013-28

Cite as: Sasou, A. (2013) Evaluation of fundamental validity in applying AR-HMM with automatic topology generation to pathology voice analysis. Proc. Interspeech 2013, 1673-1676, doi: 10.21437/Interspeech.2013-28

@inproceedings{sasou13_interspeech,
  author={Akira Sasou},
  title={{Evaluation of fundamental validity in applying AR-HMM with automatic topology generation to pathology voice analysis}},
  year=2013,
  booktitle={Proc. Interspeech 2013},
  pages={1673--1676},
  doi={10.21437/Interspeech.2013-28}
}