Belief-based nonlinear rescoring in Thai speech understanding

This paper proposes an approach to improve speech understanding based on rescoring of N-best semantic hypotheses. In rescoring, probabilities produced by an understanding component are combined with additional probabilities derived from system beliefs. While a normal rescoring approach is to multiply or linearly interpolate with belief probabilities, this paper shows that probabilities from various sources are better combined using a nonlinear estimator. Using the proposed model together with a dialogue-state dependent semantic model shows a significant improvement when applying to a Thai interactive hotel reservation agent (TIRA), the first spoken dialogue system in Thai language.