An Overview of Statistical Language Model Adaptation

Speech recognition performance is severely a ected when the lexical, syntactic, or semantic characteristics of the discourse in the training and recognition tasks di er. The aim of language model adaptation is to exploit speci c, albeit limited, knowledge about the recognition task to compensate for this mismatch. More generally, an adaptive language model seeks to maintain an adequate representation of the current task domain under changing conditions involving potential variations in vocabulary, syntax, content, and style. This paper presents an overview of the major approaches proposed to address this issue, and o ers some perspectives regarding their comparative merits and associated trade-o s.