| Project/Area Number |
17H03192
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Dynamics/Control
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| Research Institution | Kyushu University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
木庭 洋介 九州大学, 工学研究院, 技術専門職員 (20380602)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥17,940,000 (Direct Cost: ¥13,800,000、Indirect Cost: ¥4,140,000)
Fiscal Year 2019: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2018: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2017: ¥11,310,000 (Direct Cost: ¥8,700,000、Indirect Cost: ¥2,610,000)
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| Keywords | 発声障害診断 / 音声生成解析 / 声帯振動 / 自励振動 / 集中系モデル / 喉頭がん / 声帯ポリープ / 声帯麻痺 / 診断 / 発声障害 / 音声生成 |
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| Outline of Final Research Achievements |
In this study, we propose a method for diagnosing vocal dysfunction in which air in a vocal tract is modeled as a concentrated mass model and flow velocity waveform in the vocal folds are inversely analyzed from voice data measured by a microphone. During the research period, a vocalization model (simple model and detailed model) imitating human vocalization mechanism was produced, and the velocity waveform in the vocal cords was inversely analyzed from the sound at the model exit, and the validity of the diagnostic method was verified. Then, we analyzed the velocity waveform in the vocal cords using the voice data of dysphonia patients, and classified the velocity waveforms for each disease such as vocal cord polyps, polyp-like vocal cords, and vocal cord paralysis. Furthermore, we made a speech generation analysis model including vocal cord vibrations, clarified the human vocalization mechanism, and simulated some vocalization disorders.
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| Academic Significance and Societal Importance of the Research Achievements |
喉頭がん，声帯ポリープなどの発声障害は早期発見が重要である．発声障害の診断では，画像診断の他に音声検査（GRBAS尺度）が行われているが客観性に乏しい．音声から声帯部の流速変動（声帯音源波）を推定できれば，声帯の病変が直接的に影響するため，病変をより正確に知ることができる．
本研究では，マイクで測定した音声から声帯音源波形を逆解析する診断手法を提案した．提案手法は計算負荷の少ない診断手法であるため，音声障害診断への適用のみならず集団検診やインターネットを利用したスクリーニング診断にも展開できる．また，音声生成解析モデルを作成し，発声メカニズムや疾患による音声変化のメカニズムの一部を明確にした．
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