Sixth International Conference on Spoken Language Processing (ICSLP 2000)

Beijing, China
October 16-20, 2000

Automatic Transcription of Lecture Speech Using Topic-Independent Language Modeling

Kazuomi Kato, Hiroaki Nanjo, Tatsuya Kawahara

School of Informatics, Kyoto University, Japan

We approach lecture speech recognition with a topic-independent language model and its adaptation. As lecture speech has its characteristic style that is different from newspapers and conversations, dedicated language modeling is needed. The problem is that, although lectures have many keywords specific to the topic and fields, available corpus of each domain is limited in size. Thus, we introduce topic-independent modeling with a vocabulary selection mechanism based on a mutual information criterion. It realizes better coverage and accuracy with small complexity than the conventional word frequency-based method. This baseline model is adapted to specific lectures using preprint texts. We have tried automatic transcription of oral presentations and achieved a word error rate of 23.6% on the average.


Full Paper

Bibliographic reference.  Kato, Kazuomi / Nanjo, Hiroaki / Kawahara, Tatsuya (2000): "Automatic transcription of lecture speech using topic-independent language modeling", In ICSLP-2000, vol.1, 162-165.