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From rule-based to statistical grammars: Continuous improvement of large-scale spoken dialog systems

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6 Author(s)
D. Suendermann ; SpeechCycle Labs, New York, USA ; K. Evanini ; J. Liscombe ; P. Hunter
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Statistical Spoken Language Understanding grammars (SSLUs) are often used only at the top recognition contexts of modern large-scale spoken dialog systems. We propose to use SSLUs at every recognition context in a dialog system, effectively replacing conventional, manually written grammars. Furthermore, we present a methodology of continuous improvement in which data are collected at every recognition context over an entire dialog system. These data are then used to automatically generate updated context-specific SSLUs at regular intervals and, in so doing, continually improve system performance over time. We have found that SSLUs significantly and consistently outperform even the most carefully designed rule-based grammars in a wide range of contexts in a corpus of over two million utterances collected for a complex call-routing and troubleshooting dialog system.

Published in:

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

Date of Conference:

19-24 April 2009