Using Dialog-Level Knowledge Sources to Improve Speech Recognition
Abstract
We motivate and describe an implementation of the MINDS speech recognition system. MINDS uses knowledge of dialog structures, user goals and focus in a problem solving situation. The knowledge is combined to form predictions which translate into dynamically generated semantic network grammars. An experiment evaluated recognition accuracy given different levels of knowledge as constraints. Our results show that speech recognition accuracy improves dramatically, when the maximally constrained dynamic network grammar is used to process the speech input signal.
Cite
Text
Hauptmann et al. "Using Dialog-Level Knowledge Sources to Improve Speech Recognition." AAAI Conference on Artificial Intelligence, 1988.Markdown
[Hauptmann et al. "Using Dialog-Level Knowledge Sources to Improve Speech Recognition." AAAI Conference on Artificial Intelligence, 1988.](https://mlanthology.org/aaai/1988/hauptmann1988aaai-using/)BibTeX
@inproceedings{hauptmann1988aaai-using,
title = {{Using Dialog-Level Knowledge Sources to Improve Speech Recognition}},
author = {Hauptmann, Alexander G. and Young, Sheryl R. and Ward, Wayne H.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1988},
pages = {729-733},
url = {https://mlanthology.org/aaai/1988/hauptmann1988aaai-using/}
}