誤り検出とコンテキスト適応誤り訂正による音声認識における希少語認識精度改善

• Item type: SIG Technical Reports(1)
• 公開日: 2023-11-25
• タイトル: 誤り検出とコンテキスト適応誤り訂正による音声認識における希少語認識精度改善
• タイトル / 言語: en
• タイトル: Enhancing Recognition of Rare Words in ASR through Error Detection and Context-Aware Error Correction
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 分野横断（1）
• 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: 名古屋大学情報学研究科知能システム学専攻
• 著者所属: 名古屋大学情報基盤センター
• 著者所属: 名古屋大学情報基盤センター
• 著者所属(英): en
• 著者所属(英): Graduate School of Informatics, Nagoya University
• 著者所属(英): en
• 著者所属(英): Information Technology Center, Nagoya University
• 著者所属(英): en
• 著者所属(英): Information Technology Center, Nagoya University
• 著者名: 何, 嘉俊; 楊, 沢坤; 戸田, 智基
• 著者名(英): Jiajun, He; Zekun, Yang; Tomoki, Toda
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Automatic speech recognition (ASR) systems often suffer from errors, particularly when recognizing rare words. These errors can be detrimental to downstream tasks such as keyword spotting and language understanding. To alleviate this issue, we propose an ASR error correction method that improves rare word recognition based on error detection and context-aware error correction. Our proposed method limits decoding to only the positions where corrections are required to minimize unnecessary computations. A rare word list is also used to provide additional contextual information for the model to correct errors related to rare words. Experimental results demonstrate that our proposed method outperforms previous works by a large margin in word error rate (WER) on five public datasets while also maintaining a reasonable inference speed. Additionally, the proposed method shows reasonable robustness across different ASR systems.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Automatic speech recognition (ASR) systems often suffer from errors, particularly when recognizing rare words. These errors can be detrimental to downstream tasks such as keyword spotting and language understanding. To alleviate this issue, we propose an ASR error correction method that improves rare word recognition based on error detection and context-aware error correction. Our proposed method limits decoding to only the positions where corrections are required to minimize unnecessary computations. A rare word list is also used to provide additional contextual information for the model to correct errors related to rare words. Experimental results demonstrate that our proposed method outperforms previous works by a large margin in word error rate (WER) on five public datasets while also maintaining a reasonable inference speed. Additionally, the proposed method shows reasonable robustness across different ASR systems.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10115061
• 書誌情報: 研究報告自然言語処理（NL）; 巻 2023-NL-258, 号 10, p. 1-6, 発行日 2023-11-25
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8779
• 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