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Human language is a combination of elemental languages/domains/styles thatchange across and sometimes within discourses. Language models, which play acrucial role in speech recognizers and machine translation systems, areparticularly sensitive to such changes, unless some form of adaptation takesplace. One approach to speech language model adaptation is self-training, inwhich a language model's parameters are tuned based on automaticallytranscribed audio. However, transcription errors can misguide self-training,particularly in challenging settings such as conversational speech. In thiswork, we propose a model that considers the confusions (errors) of the ASRchannel. By modeling the likely confusions in the ASR output instead of usingjust the 1-best, we improve self-training efficacy by obtaining a more reliablereference transcription estimate. We demonstrate improved topic-based languagemodeling adaptation results over both 1-best and lattice self-training usingour ASR channel confusion estimates on telephone conversations.

Estimating Confusions in the ASR Channel for Improved Topic-based Language Model Adaptation