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

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概要

• 論文の詳細を見る

• 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.

• 一般社団法人情報処理学会の論文
• 2009-02-15

著者

• Hajime Tsukada

  Ntt Communication Science Laboratories

• Katsuhito Sudoh

  NTT Communication Science Laboratories

• Hideki Isozaki

  NTT Communication Science Laboratories

関連論文

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

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