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Investigation of Adapter for Automatic Speech Recognition in Noisy Environment
https://ipsj.ixsq.nii.ac.jp/records/231311
https://ipsj.ixsq.nii.ac.jp/records/231311025333c0-fb12-4aa1-9154-cce79aa9f901
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-SLP23149019.pdf (206.7 kB)
2025年11月25日からダウンロード可能です。
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Copyright (c) 2023 by the Information Processing Society of Japan
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| 非会員：¥660, IPSJ:学会員：¥330, SLP:会員：¥0, DLIB:会員：¥0 | ||
| Item type | SIG Technical Reports(1) | |||||||||
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| 公開日 | 2023-11-25 | |||||||||
| タイトル | ||||||||||
| タイトル | Investigation of Adapter for Automatic Speech Recognition in Noisy Environment | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Investigation of Adapter for Automatic Speech Recognition in Noisy Environment | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | 分野横断（2） | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||||
| 資源タイプ | technical report | |||||||||
| 著者所属 | ||||||||||
| Graduate School of Informatics, Kyoto University | ||||||||||
| 著者所属 | ||||||||||
| Graduate School of Informatics, Kyoto University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Informatics, Kyoto University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Informatics, Kyoto University | ||||||||||
| 著者名 |
Hao, Shi
× Hao, Shi
× Tatsuya, Kawahara
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| 著者名(英) |
Hao, Shi
× Hao, Shi
× Tatsuya, Kawahara
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Adapting an automatic speech recognition (ASR) system to unseen noise environments is crucial. Integrating adapters into neural networks has emerged as a potent technique for transfer learning. This study thoroughly investigates adapter-based ASR adaptation in noisy environments. We conducted experiments using the CHiME-4 dataset. The results show that inserting the adapter in the shallow layer yields superior effectiveness, and there is no significant difference between adapting solely within the shallow layer and adapting across all layers. The simulated data helps the system to improve its performance under real noise conditions. Nonetheless, when the amount of data is the same, the real data is more effective than the simulated data. Multi-condition training is still useful for adapter training. Furthermore, integrating adapters into speech enhancement-based ASR systems yields substantial improvements. | |||||||||
| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Adapting an automatic speech recognition (ASR) system to unseen noise environments is crucial. Integrating adapters into neural networks has emerged as a potent technique for transfer learning. This study thoroughly investigates adapter-based ASR adaptation in noisy environments. We conducted experiments using the CHiME-4 dataset. The results show that inserting the adapter in the shallow layer yields superior effectiveness, and there is no significant difference between adapting solely within the shallow layer and adapting across all layers. The simulated data helps the system to improve its performance under real noise conditions. Nonetheless, when the amount of data is the same, the real data is more effective than the simulated data. Multi-condition training is still useful for adapter training. Furthermore, integrating adapters into speech enhancement-based ASR systems yields substantial improvements. | |||||||||
| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN10442647 | |||||||||
| 書誌情報 |
研究報告音声言語情報処理（SLP） 巻 2023-SLP-149, 号 19, p. 1-6, 発行日 2023-11-25 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 2188-8663 | |||||||||
| Notice | ||||||||||
| SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||||
| 出版者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
