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

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 textbased NER to ASR results in NER Fmeasure by improving precision. These results show that the proposed method is effective in NER for noisy inputs. © 2006 Association for Computational Linguistics.