Article details

Research area
Natural language & AI

International Conference on Acoustics, Speech, and Signal Processing, 1996.


Barbara Peskin, Sean Connolly, Larry Gillick, Stephen Lowe, Don McAllaster, Venki Nagesha, Paul van Mulbregt, Steven Wegmann

Improvements in switchboard recognition and topic identification


We revisit a topic identification test on the Switchboard Corpus first reported by Gillick et al. (see Proc. ICASSP-93, 1993 and ARPA Workshop on Human Language Technology, 1993). This approach to topic ID uses a large vocabulary continuous speech recognizer as a front-end to transcribe the speech and then scores the transcripts using a set of topic-specific language models. Our recognition of conversational telephone speech has improved dramatically in the three years since the original test, dropping from word error rates in the 90%’s to those in the 40%’s. Changing only the recognition engine but otherwise leaving our 1993 topic ID system in place, the resulting rate of message misclassification drops from 33/120 in 1993 down to 1/120 now-the same error rate that we obtain from the true transcriptions. This paper describes the topic classification test and the many improvements to the recognition engine that made such a dramatic reduction possible.

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