Named Entity Recognition from Speech Using Discriminative Models and Speech Recognition Confidence

• Katsuhito, Sudoh

• Hajime, Tsukada

• Hideki, Isozaki

書誌事項

タイトル別名

• 情報抽出、構造化

抄録

This paper proposes a discriminative named entity recognition (NER) method from automatic speech recognition (ASR) results. The proposed method uses the confidence of the ASR result as a feature that represents whether each word has been correctly recognized. Consequently, it provides robust NER for the noisy input caused by ASR errors. The NER model is trained using ASR results and reference transcriptions with named entity (NE) annotation. Experimental results using support vector machines (SVMs) and speech data from Japanese newspaper articles show that the proposed method outperformed a simple application of text-based NER to the ASR results, especially in terms of improving precision.

This paper proposes a discriminative named entity recognition (NER) method from automatic speech recognition (ASR) results. The proposed method uses the confidence of the ASR result as a feature that represents whether each word has been correctly recognized. Consequently, it provides robust NER for the noisy input caused by ASR errors. The NER model is trained using ASR results and reference transcriptions with named entity (NE) annotation. Experimental results using support vector machines (SVMs) and speech data from Japanese newspaper articles show that the proposed method outperformed a simple application of text-based NER to the ASR results, especially in terms of improving precision.

収録刊行物

• 情報処理学会論文誌

  情報処理学会論文誌 50 (2), 534-543, 2009-02-15

キーワード

• 特集：音声ドキュメント処理

詳細情報

• CRID: 1050845762811317888
• NII論文ID: 110007970349
• NII書誌ID: AN00116647
• ISSN: 18827764
• Web Site: http://id.nii.ac.jp/1001/00009272/
• 本文言語コード: en
• 資料種別: journal article
• データソース種別:
  • IRDB
  • CiNii Articles