Language modeling for dialog system

Language modeling for speech recognizer in dialog systems can take two forms. Human input can be constrained through a directed dialog, allowing the decoder to use a state-specific language model to improve recognition accuracy. Mixedinitiative systems allow for human input that while domainspecific might not be state-specific. Nevertheless, for the most part human input to a mixed-initiative system is predictable, particularly when given information about the immediately preceding system prompt. The work reported in this paper addresses the problem of balancing state-specific and general language modeling in a mixed-initiative dialog system. By incorporating dialog state adaptation of the language model, we have reduced the recognition error rate by 11.5%.

[1]  Paolo Baggia,et al.  Specialized language models using dialogue predictions , 1996, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  Jochen Peters,et al.  Semantic clustering for adaptive language modeling , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[4]  Akira Kurematsu,et al.  Language model selection based on the analysis of Japanese spontaneous speech on travel arrangement task , 1999, EUROSPEECH.

[5]  Alexander I. Rudnicky,et al.  Creating natural dialogs in the carnegie mellon communicator system , 1999, EUROSPEECH.

[6]  Joshua Goodman,et al.  Putting it all together: language model combination , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[7]  Shimei Pan,et al.  Empirically Evaluating an Adaptable Spoken Dialogue System , 1999, ArXiv.

[8]  Ronald Rosenfeld,et al.  Using story topics for language model adaptation , 1997, EUROSPEECH.

[9]  Shrikanth S. Narayanan,et al.  Language model adaptation for spoken language systems , 1998, ICSLP.

[10]  Thomas Hofmann,et al.  Topic-based language models using EM , 1999, EUROSPEECH.

[11]  Ronald Rosenfeld,et al.  Statistical language modeling using the CMU-cambridge toolkit , 1997, EUROSPEECH.

[12]  Xuedong Huang,et al.  Improved topic-dependent language modeling using information retrieval techniques , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).