There are various kinds of adaptation which can be used to enhance the performance of automatic speech recognizers. This paper is about pronunciation adaptation at the lexical level, i.e. about modeling pronunciation variation at the lexical level. In the early years of automatic speech recognition (ASR) research, the amount of pronunciation variation was limited by using isolated words. Since the focus gradually shifted from isolated words to conversational speech, the amount of pronunciation variation present in the speech signals has increased, as has the need to model it. This is reflected by the growing attention for this topic. In this paper, an overview of the studies on lexicon adaptation is presented. Furthermore, many examples are mentioned of situations in which lexicon adaptation is likely to improve the performance of speech recognizers. Finally, it is argued that some assumptions made in current standard ASR systems are not in line with the properties of the speech signals. Consequently, the problem of pronunciation variation at the lexical level probably cannot be solved by simply adding new transcriptions to the lexicon, as it is generally done at the moment.

This paper builds on previous work aimed at unraveling the structure of the speech signal using probabilistic representations. The context of this work is a multi-pass speech recognition system in which a phone lattice is created and used as a basis for a lexical decoding pass (search) that allows symbolic mismatches at certain costs. The focus is on the optimization of the costs of the phone insertions, deletions and substitutions that are used in the lexical decoding pass. Two optimization approaches are presented, one related to a multi-pass computational model for human speech recognition, the other based on a decoding that minimizes Bayes' risks. In the final section, the advantages of the two optimization methods are discussed and compared.