文章基本信息

标题：Named Entity Recognition from Speech Using Discriminative Models and Speech Recognition Confidence
本地全文：下载
作者：Katsuhito Sudoh ; Hajime Tsukada ; Hideki Isozaki 等
期刊名称：Information and Media Technologies
电子版ISSN：1881-0896
出版年度：2009
卷号：4
期号：2
页码：475-484
DOI：10.11185/imt.4.475
出版社：Information and Media Technologies Editorial Board
摘要：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.