In this article we propose an original approach that allows the decoding of Automatic Speech Recognition Graphs by using a constructive algorithm based on ant colonies. In classical approaches, when a graph is decoded with higher order language models; the algorithm must expand the graph in order to develop each new observed n-gram. This extension process increases the computation time and memory consumption. We propose to use an ant colony algorithm in order to explore ASR graphs with a new language model, without the necessity of expanding it. We first present results based on the TED English corpus where 2-grams graph are decoded with a 4-grams language model. Then, we show that our approach performs better than a conventional Viterbi algorithm when computing time is constrained and allows a highly threaded decoding process with a single graph and a strict control of computation time and memory consumption.
Cite as: Lecouteux, B., Schwab, D. (2015) Ant colony algorithm applied to automatic speech recognition graph decoding. Proc. Interspeech 2015, 2122-2126, doi: 10.21437/Interspeech.2015-479
@inproceedings{lecouteux15_interspeech,
author={Benjamin Lecouteux and Didier Schwab},
title={{Ant colony algorithm applied to automatic speech recognition graph decoding}},
year=2015,
booktitle={Proc. Interspeech 2015},
pages={2122--2126},
doi={10.21437/Interspeech.2015-479}
}