An effective and robust approach to Mandarin spoken language understanding in specific domain

This paper describes an effective and robust approach based on finite state word network for Mandarin spoken language understanding (SLU) in specific domain. A kind of syntax for grammar representation is defined to efficiently specify the utterances which may be spoken in a task. Moreover, arbitrary semantic meaning can be added into grammars conveniently. Then, the grammars are complied into a finite state word network, which contains both literal and semantic information defined by the grammars. A robust parser is implemented based on 3-dimensional dynamic programming. Given a transcription from an automatic speech recognition (ASR) system, the parser searches for the best path in the word network that matches the recognition text most closely. The semantic meaning of the transcription can then be extracted from the best path. Experimental results demonstrate the good performance and robustness of the proposed approach on a Mandarin SLU task.