| 研究課題/領域番号 |
19K12035
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| 研究機関 | 国立研究開発法人情報通信研究機構 |
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研究代表者 |
LU Xugang 国立研究開発法人情報通信研究機構, 先進的音声翻訳研究開発推進センター先進的音声技術研究室, 主任研究員 (20362022)
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| 研究期間 (年度) |
2019-04-01 – 2022-03-31
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| キーワード | Speaker embedidng / Unsupervised adaptation |
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| 研究実績の概要 |
In speech separation, one of the most important cues is the speaker information. In order to extract speaker information, we have constructed a speaker embedding system based on a large scale data corpus. Based on the embedding system, speaker characteristic for each input utterance could be estimated. This speaker embedding feature could be incorporated for mixed speech for speech (target speaker) extraction. Moreover, concerning speech may be from different recording environments, we proposed a new distance metric for unsupervised domain adaptation technique, and preliminary experiments on cross-channel domain spoken language recognition task showed promising results.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
In last year, we found the importance of speaker characteristics in speech separation. We further focus on the techniques for speaker feature embedding. Based on a large and public speech data corpus for speaker recognition, we built a speaker embedding system. In the system, we proposed a generative and discriminative learning framework in order to explore discriminative and robust speaker information.
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| 今後の研究の推進方策 |
Based on our previous investigations, we will further carry out studies in the following two directions: (1) Based on the speaker embeddings, we will study algorithms for target speaker speech tracking and separation, (2) since the speech recording channels may be different from session to session, we will investigate the model adaptation for cross-channel acoustic environments problem.
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| 次年度使用額が生じた理由 |
Due to the COVID 19, the cost for business trip and workstations for data recordings were not used. In this new year plan, the workstation will be bought.
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