Paper: Incorporating Speech Recognition Confidence Into Discriminative Named Entity Recognition Of Speech Data

ACL ID P06-1078
Title Incorporating Speech Recognition Confidence Into Discriminative Named Entity Recognition Of Speech Data
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

This paper proposes a named entity recog- nition (NER) method for speech recogni- tion results that uses confidence on auto- matic speech recognition (ASR) as a fea- ture. The ASR confidence feature indi- cates whether each word has been cor- rectly recognized. The NER model is trained using ASR results with named en- tity (NE) labels as well as the correspond- ing transcriptions with NE labels. In ex- periments 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.