Subjective intelligibility estimation of noisy speech using objective measures
Project/Area Number |
25330182
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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
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Research Institution | Yamagata University |
Principal Investigator |
|
Research Collaborator |
KOBAYASHI Yosuke 室蘭工業大学, 大学院工学研究科, 助教 (10735103)
|
Project Period (FY) |
2013-04-01 – 2016-03-31
|
Project Status |
Completed (Fiscal Year 2015)
|
Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
|
Keywords | 音声了解度 / 客観推定 / 機械学習 / クラスタリング / 音声品質推定 / 客観的推定 / 回帰分析 / 二者択一型試験 / 加算雑音 / 推定 / 二者択一 / 雑音 / 音声特徴量 / Articulation Index Band |
Outline of Final Research Achievements |
We investigated a method to estimate speech intelligibility of noisy speech using objective measures without human listeners. We investigated clustering noise according to its effect on intelligibility, and using separate mapping function from objective measure to subjective intelligibility by cluster. We found that three clusters for this purpose is appropriate. By training a relatively simple mapping function per cluster, the estimation accuracy improved significantly compared to using a single function for all noise. The accuracy improved further by using support vector regression, a well known method in machine learning, as the mapping function. We also investigated on estimation without the use of clean speech samples, and found that accuracy high enough for practical use is possible.
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Report
(4 results)
Research Products
(22 results)