Combining Knowledge Sources to Reorder N-Best Speech Hypothesis Lists

COMBINING KNOWLEDGE SOURCES TO REORDER N-BEST SPEECH HYPOTHESIS LISTS Manny Raynez a, David Carter 1, Vassilios Digalakis 2, Patti Price 2 (1) SRI International, Suite 23, Millers Yard, Cambridge CB2 1RQ, UK (2) SRI International, Speech Technology and Research Laboratory, 333 Ravenswood Ave., Menlo Park, CA 94025-3493, USA ABSTRACT A simple and general method is described that can combine different knowledge sources to reorder N-best lists of hypothe- ses produced by a speech recognizer. The method is automat- ically trainable, acquiring information from both positive and negative examples. In experiments, the method was tested on a 1000-utterance sample of unseen ATIS data. 1. INTRODUCTION During the last few years, the previously separate fields of speech and natural language processing have moved much closer together, and it is now common to see integrated sys- tems containing components for both speech recognition and language processing. An immediate problem is the nature of the interface between the two. A popular solution has been the N-best list-for example, [9]; for some N, the speech recog- nizer hands the language processor the N utterance hypothe- ses it considers most plausible. The recognizer chooses the hypotheses on the basis of the acoustic information in the in- put signal and, usually, a simple language model such as a bi- gram grammar. The language processor brings more sophis- ticated linguistic knowledge sources to bear, typically some form of syntactic and/or semantic analysis, and uses them to choose the most plausible member of the N-best list. We will call an algorithm that selects a member of the N-best list a preference method. The most common preference method is to select the highest member of the list that receives a valid semantic analysis. We will refer to this as the "highest-in- coverage" method. Intuitively, highest-in-coverage seems a promising idea. However, practical experience shows that it is surprisingly hard to use it to extract concrete gains. For example, a recent paper [8] concluded that the highest-in- coverage candidate was in terms of the word error rate only very marginally better than the one the recognizer considered best. In view of the considerable computational overhead re- quired to perform linguistic analysis on a large number of speech hypotheses, its worth is dubious. In this paper, we will describe a general strategy for con- structing a preference method as a near-optimal combination of a number of different knowledge sources. By a "knowledge source", we will mean any well-defined procedure that asso- ciates some potentially meaningful piece of information with a given utterance hypothesis H. Some examples of knowledge sources are • The plausibility score originally assigned to H by the recognizer • The sets of surface unigrams, bigrams and trigrams present in H • Whether or not H receives a well-formed syntac- tic/semantic analysis • If so, properties of that analysis The methods described here were tested on a 1001-utterance unseen subset of the ATIS corpus; speech recognition was performed using SRI's DECIPHER TM recognizer [7, 5], and linguistic analysis by a version …

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