Factorization of Language Constraints in Speech Recognition

FACTORIZATION OF LANGUAGE CONSTRAINTS IN SPEECH RECOGNITION Roberto Pieraccini and Chin-Hui Lee Speech Research Department AT&T Bell Laboratories Murray Hill, NJ 07974, USA ABSTRACT Integration of language constraints into a large vocabulary speech recognition system often leads to prohibitive complexity. We propose to factor the constraints into two components. The first is characterized by a covering grammar which is small and easily integrated into existing speech recognizers. The recognized string is then decoded by means of an efficient language post-processor in which the full set of constraints is imposed to correct possible errors introduced by the speech recognizer. 1. Introduction In the past, speech recognition has mostly been applied to small domain tasks in which language constraints can be characterized by regular grammars. All the knowledge sources required to perform speech recognition and understanding, including acoustic, phonetic, lexical, syntactic and semantic levels of knowledge, are often encoded in an integrated manner using a finite state network (FSN) representation. Speech recognition is then performed by finding the most likely path through the FSN so that the acoustic distance between the input utterance and the recognized string decoded from the most likely path is minimized. Such a procedure is also known as maximum likelihood decoding, and such systems are referred to as integrated systems. Integrated systems can generally achieve high accuracy mainly due to the fact that the decisions are delayed until enough information, derived from the knowledge sources, is available to the decoder. For example, in an integrated system there is no explicit segmentation into phonetic units or words during the decoding process. All the segmentation hypotheses consistent with the introduced constraints are carried on until the final decision is made in order to maximize a global function. An example of an integrated system was HARPY (Lowerre, 1980) which integrated multiple levels of knowledge into a single FSN. This produced relatively high performance for the time, but at the cost of multiplying out constraints in a manner that expanded the grammar beyond reasonable bounds for even moderately complex domains, and may not scale up to more complex tasks. Other examples of integrated systems may be found in Baker (1975) and Levinson (1980). On the other hand modular systems clearly separate the knowledge sources. Different from integrated systems, a modular system usually make an explicit use of the constraints at each level of knowledge for making hard decisions. For instance, in modular systems there is an explicit segmentation into phones during an early stage of the decoding, generally followed by lexical access, and by syntactic/semantic parsing. While a modular system, like for instance HWIM (Woods, 1976) or HEARSAY-II (Reddy, 1977) may be the only solution for extremely large tasks when the size of the vocabulary is on the order of 10,000 words or more (Levinson, 1988), it generally achieves lower performance than an integrated system in a restricted domain task (Levinson, 1989). The degradation in performance is mainly due to the way errors propagate through the system. It is widely agreed that …

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