- K. Zechner, A. Waibel. 2002. Using Chunk Based Partial Parsing of Spontaneous Speech in Unrestricted Domains for Reducing Word Error Rate in Speech Recognition. Abstract: In this paper, we present a chunk based partial parsing system for spontaneous, conversational speech in unrestricted domains. We show that the chunk parses produced by this parsing system can be usefully applied to the task of reranking Nbest lists from a speech recognizer, using a combination of chunk-based n-gram model scores and chunk coverage scores. The input for the system is Nbest lists generated from speech recognizer lattices. The hypotheses from the Nbest lists are tagged for part of speech, "cleaned up" by a preprocessing pipe, parsed by a part of speech based chunk parser, and rescored using a backpropagation neural net trained oll the chunk based scores. Finally, the reranked Nbest lists are generated. The results of a system evaluation are promising in that a chunk accuracy of 87.4% is achieved and the best performance on a randomly selected test set is a decrease in word error rate of 0.3 percent (absolute), measured on the new first hypotheses in the reranked Nbest lists. 1 I n t r o d u c t i o n In the area of parsing spontaneous speech, most work so far has primarily focused on dealing with texts within a narrow, well-defined domain. Full scale parsers for spontaneous speech face severe difficulties due to the intrinsic nature of spoken language (e.g., false starts, hesitations, ungrammaticalities), in addition to the well-known complexities of large coverage parsing systems in general (Lavie, 1996; Light, 1996). An even more serious problem is the imperfect word accuracy of speech recognizers, particularly when faced with spontaneous speech over a large vocabuhtry and over a low bandwidth channel. This is particularly the case for the SWITCHBOARD database (Godfrey et al., 1992) which we mainly used for development, testing, and evaluation of our ~,;ystem. Current state-of-the-art recognizers exhibit word error rates (WER 1) for this corpus of approx1The word error rate (WER in %) is defined as follows: K l a u s Z e c h n e r and A l e x W a i b e l Language Technologies I n s t i t u t e Carnegie Mellon Univers i ty
