A unified context-free grammar and n-gram model for spoken language processing

While context-free grammars (CFGs) remain as one of the most important formalisms for interpreting natural language, word n-gram models are surprisingly powerful for domain-independent applications. We propose to unify these two formalisms for both speech recognition and spoken language understanding (SLU). With portability as the major problem, we incorporated domain-specific CFGs into a domain-independent n-gram model that can improve the generalizability of the CFG and the specificity of the n-gram. In our experiments, the unified model can significantly reduce the test set perplexity from 378 to 90 in comparison with a domain-independent word trigram. The unified model converges well when domain-specific data becomes available. The perplexity can be further reduced from 90 to 65 with a limited amount of domain-specific data. While we have demonstrated excellent portability, the full potential of our approach lies in its unified recognition and understanding that we are investigating.