Sixth International Conference on Spoken Language Processing (ICSLP 2000)

Beijing, China
October 16-20, 2000

Effects of Word String Language Models on Noisy Broadcast News Speech Recognition

Kazuyuki Takagi, Rei Oguro, Kazuhiko Ozeki

The University of Electro-Communications, Chofu, Tokyo, Japan

In this paper, we present the results that our n-gram based word string language model, combined with speaker and noise adaptation of the acoustic model, improves recognition performance of noisy broadcast news speech. The focus was brought into a remedy against recognition errors of short words. The word string language models based on POS and n-gram fre- quency reduced deletion errors by 17%, insertion errors by 20%, and substitution errors by 3% in Japanese TV broadcast news speech recognition.

Full Paper

Bibliographic reference.  Takagi, Kazuyuki / Oguro, Rei / Ozeki, Kazuhiko (2000): "Effects of word string language models on noisy broadcast news speech recognition", In ICSLP-2000, vol.1, 154-157.