Ye-Yi Wang, Milind Mahajan, and Xuedong Huang
While context-free grammars (CFGs) remain as one of the most
important formalisms for interpreting natural language, word ngram
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 domainspecific
CFGs into a domain-independent n-gram model that can
improve generalizability of the CFG and specificity of the ngram.
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 the 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.
In Proc. of Int. Conf. on Acoustics, Speech, and Signal Processing
Publisher Institute of Electrical and Electronics Engineers, Inc.
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