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ファイル | 記述 | サイズ | フォーマット | |
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j.csl.2012.02.003.pdf | 517.86 kB | Adobe PDF | 見る/開く |
タイトル: | A monotonic statistical machine translation approach to speaking style transformation |
著者: | Neubig, Graham Akita, Yuya Mori, Shinsuke Kawahara, Tatsuya https://orcid.org/0000-0002-2686-2296 (unconfirmed) |
キーワード: | Rich transcription Speaking style transformation Disfluency detection Weighted finite state transducers Monotonic machine translation |
発行日: | Oct-2012 |
出版者: | Elsevier Ltd. |
誌名: | Computer Speech & Language |
巻: | 26 |
号: | 5 |
開始ページ: | 349 |
終了ページ: | 370 |
抄録: | This paper presents a method for automatically transforming faithful transcripts or ASR results into clean transcripts for human consumption using a framework we label speaking style transformation (SST). We perform a detailed analysis of the types of corrections performed by human stenographers when creating clean transcripts, and propose a model that is able to handle the majority of the most common corrections. In particular, the proposed model uses a framework of monotonic statistical machine translation to perform not only the deletion of disfluencies and insertion of punctuation, but also correction of colloquial expressions, insertions of omitted words, and other transformations. We provide a detailed description of the model implementation in the weighted finite state transducer (WFST) framework. An evaluation of the proposed model on both faithful transcripts and speech recognition results of parliamentary and lecture speech demonstrates the effectiveness of the proposed model in performing the wide variety of corrections necessary for creating clean transcripts. |
著作権等: | © 2012 Elsevier Ltd. This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 |
URI: | http://hdl.handle.net/2433/157359 |
DOI(出版社版): | 10.1016/j.csl.2012.02.003 |
出現コレクション: | 学術雑誌掲載論文等 |
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