Adaptive categorical understanding for spoken dialogue systems
In this paper, the speech understanding problem in the context of a spoken dialogue system is formalized in a maximum likelihood framework. Off-line adaptation of stochastic language models that interpolate dialogue state specific and general application-level language models is proposed. Word and dialogue-state n-grams are used for building categorical understanding and dialogue models, respectively. Acoustic confidence scores are incorporated in the understanding formulation. Problems due to data sparseness and out-of-vocabulary words are discussed. The performance of the speech recognition and understanding language models are evaluated with the "Carmen Sandiego" multimodal computer game corpus. Incorporating dialogue models reduces relative understanding error rate by 15%-25%, while acoustic confidence scores achieve a further 10% error reduction for this computer gaming application.