2021 Fiscal Year Annual Research Report
Construction of a computational model to deal with the cocktail-party problem for intelligent speech interface
| Project/Area Number |
19K12035
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| Research Institution | National Institute of Information and Communications Technology |
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Principal Investigator |
LU Xugang 国立研究開発法人情報通信研究機構, ユニバーサルコミュニケーション研究所先進的音声翻訳研究開発推進センター, 主任研究員 (20362022)
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| Project Period (FY) |
2019-04-01 – 2022-03-31
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| Keywords | Generative model / Discriminative model / Model coupling |
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| Outline of Annual Research Achievements |
In cocktail party scenarios, many information need to be explored in order to identify different speech (or sound) sources, in particular, who is speaking (speaker information) is one of the most important information for identifying speech sources. In order to combine advantages of both discriminative and generative classifier models for speakers, we proposed to couple a generative model in a discriminative learning for speaker recognition. Our framework showed a large improvement compared with state of the art models.
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Research Products
(2 results)
