  Human language is a combination of elemental languages/domains/styles that
change across and sometimes within discourses. Language models, which play a
crucial role in speech recognizers and machine translation systems, are
particularly sensitive to such changes, unless some form of adaptation takes
place. One approach to speech language model adaptation is self-training, in
which a language model's parameters are tuned based on automatically
transcribed audio. However, transcription errors can misguide self-training,
particularly in challenging settings such as conversational speech. In this
work, we propose a model that considers the confusions (errors) of the ASR
channel. By modeling the likely confusions in the ASR output instead of using
just the 1-best, we improve self-training efficacy by obtaining a more reliable
reference transcription estimate. We demonstrate improved topic-based language
modeling adaptation results over both 1-best and lattice self-training using
our ASR channel confusion estimates on telephone conversations.
