ANT COLONY ALGORITHM APPLIED TO AUTOMATIC SPEECH RECOGNITION GRAPH DECODING

Abstract : 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.
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Communication dans un congrès
Interspeech 2015, Sep 2015, Dresden, Germany
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Benjamin Lecouteux, Didier Schwab. ANT COLONY ALGORITHM APPLIED TO AUTOMATIC SPEECH RECOGNITION GRAPH DECODING. Interspeech 2015, Sep 2015, Dresden, Germany. 〈hal-01170535〉

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