| Project/Area Number |
24K03872
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 02060:Linguistics-related
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| Research Institution | The University of Aizu |
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Principal Investigator |
Julian Villegas 会津大学, コンピュータ理工学部, 上級准教授 (50706281)
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| Co-Investigator(Kenkyū-buntansha) |
李 勝勲 国際基督教大学, 教養学部, 上級准教授 (20770134)
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| Project Period (FY) |
2024-04-01 – 2027-03-31
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| Project Status |
Granted (Fiscal Year 2024)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2026: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2025: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2024: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | Machine Learning / Auditory / Linguistic / Phonation |
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| Outline of Research at the Start |
Audiphon aims to build the foundations of the next generation of Automatic Phonation Recognition (APR)-systems. Using machine-learning techniques on features extracted from models that represent speech at different levels in the auditory system, Audiphon will provide phonation recognition methodologies with superior accuracy relative to those currently used in the study of under-resourced languages. Outcomes of Audiphon include software tools for APR and identification of auditory representations that best capture different phonation types (creaky, modal, breathy, etc.).
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