The design and implementation of hybrid symbolic/statistical architectures is a major area of interest in current multimodal system development. Such an architecture attempts to improve multimodal recognition and disambiguation rates by using corpus-based statistics to weight the contributions from various input streams. This is in contrast to current architectures that assume independence between input streams, and combine un-weighted posterior probabilities simply by taking their cross product.
Recently a Members, Teams, Committee (MTC) approach for statistically hybridizing the Quickset multimodal system has been put forward on the basis of strong empirical results in an offline analysis. MTC uses small-dimensional input streams as Members, which in turn are input into various Teams where their conditional weights are trained. The Committee then extracts a decision from the output of the Teams. This paper discusses a fully implemented regression test of MTC within Quickset, and our modification of the approach to use more specific training features. We report a relative decrease in multimodal error rate of 30%.
Cite as: Kaiser, E.C., Cohen, P.R. (2002) Implementation testing of a hybrid symbolic/statistical multimodal architecture. Proc. 7th International Conference on Spoken Language Processing (ICSLP 2002), 173-176, doi: 10.21437/ICSLP.2002-113
@inproceedings{kaiser02_icslp,
author={Edward C. Kaiser and Philip R. Cohen},
title={{Implementation testing of a hybrid symbolic/statistical multimodal architecture}},
year=2002,
booktitle={Proc. 7th International Conference on Spoken Language Processing (ICSLP 2002)},
pages={173--176},
doi={10.21437/ICSLP.2002-113}
}