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

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概要

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• 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.

• Information and Media Technologies 編集運営会議の論文

著者

• Sudoh Katsuhito

  NTT Communication Science Laboratories

• Isozaki Hideki

  NTT Communication Science Laboratories

• Tsukada Hajime

  NTT Communication Science Laboratories

関連論文

• SVM-Based Multi-Document Summarization Integrating Sentence Extraction with Bunsetsu Elimination(Special Issue on Text Processing for Information Access)
• Named Entity Recognition from Speech Using Discriminative Models and Speech Recognition Confidence

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