Incorporating speech recognition confidence into discriminative named entity recognition of speech data

Authors:
Katsuhito Sudoh;Hajime Tsukada;Hideki Isozaki
Affiliations:
Nippon Telegraph and Telephone Corporation, Seika-cho, Keihanna Science City, Japan;Nippon Telegraph and Telephone Corporation, Seika-cho, Keihanna Science City, Japan;Nippon Telegraph and Telephone Corporation, Seika-cho, Keihanna Science City, Japan
Venue:
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Year:
2006

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Abstract

This paper proposes a named entity recognition (NER) method for speech recognition results that uses confidence on automatic speech recognition (ASR) as a feature. The ASR confidence feature indicates whether each word has been correctly recognized. The NER model is trained using ASR results with named entity (NE) labels as well as the corresponding transcriptions with NE labels. In experiments using support vector machines (SVMs) and speech data from Japanese newspaper articles, the proposed method outperformed a simple application of text-based NER to ASR results in NER F-measure by improving precision. These results show that the proposed method is effective in NER for noisy inputs.