Voice-pathology analysis based on automatic topology generation of glottal source HMM

2016 Fiscal Year Final Research Report

• Project/Area Number: 26330216
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Perceptual information processing
• Research Institution: National Institute of Advanced Industrial Science and Technology
• Principal Investigator: SASOU AKIRA 国立研究開発法人産業技術総合研究所, 知能システム研究部門, 主任研究員 (50318169)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: 音声分析 / 声帯音源 / AR-HMM

Outline of Final Research Achievements

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, 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 algorithm.

Free Research Field

音声・音響信号処理