Progress Report on the Chronus System: ATIS Benchmark Results

The speech understanding system we propose in this paper is based on the stochastic modeling of a sentence as a sequence of elemental units that represent its meaning. According to this paradigm, the original meaning of a sentence, can be decoded using a dynamic programming algorithm, although the small amount of training data currently available suggested the integration of the decoder with a more traditional technique. However, the advantage of this method consists in the development of a framework in which a closed training loop reduces the amount of human supervision in the design phase of the understanding component. The results reported here for the February 1992 DARPA ATIS test are extremely promising, considering the small amount of hand tuning the system required.

[1]  Aaron E. Rosenberg,et al.  Improved acoustic modeling for speaker independent large vocabulary continuous speech recognition , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[2]  F. R. Two Semantic Networks : Their Computation and Use for Understanding English Sentences , 2022 .

[3]  Roberto Pieraccini,et al.  Stochastic representation of semantic structure for speech understanding , 1991, Speech Commun..

[4]  Chin-Hui Lee,et al.  Acoustic modeling for large vocabulary speech recognition , 1990 .

[5]  Madeleine Bates,et al.  A Proposal for SLS Evaluation , 1989, HLT.

[6]  Enrique Vidal,et al.  Learning language models through the ECGI method , 1991, Speech Commun..

[7]  Lawrence R. Rabiner,et al.  A segmental k-means training procedure for connected word recognition , 1986, AT&T Technical Journal.

[8]  Chin-Hui Lee,et al.  Stochastic Representation of Conceptual Structure in the ATIS Task , 1991, HLT.

[9]  Chin-Hui Lee,et al.  Bayesian learning for hidden Markov model with Gaussian mixture state observation densities , 1991, Speech Commun..

[10]  Chin-Hui Lee,et al.  Automatic recognition of keywords in unconstrained speech using hidden Markov models , 1990, IEEE Trans. Acoust. Speech Signal Process..

[11]  Ralph Grishman,et al.  Analyzing language in restricted domains : sublanguage description and processing , 1986 .

[12]  Chin-Hui Lee,et al.  A speech understanding system based on statistical representation of semantics , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.