Voice-pathology analysis based on automatic topology generation of glottal source HMM
Project/Area Number |
26330216
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Research Category |
Grant-in-Aid for Scientific Research (C)
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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
|
Project Status |
Completed (Fiscal Year 2016)
|
Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2014: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
|
Keywords | 音声分析 / 声帯音源 / AR-HMM / 病的音声 / 声帯疾患 / 嗄声 / 聴覚印象評価 / 喉頭がん / GRBAS尺度 / 音声合成 / 食道発声音声 / 声質改善 |
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.
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Report
(4 results)
Research Products
(4 results)