Progress in dynamic network decoding

We show how we boosted the efficiency of the dynamic network decoder in IBM's Attila speech recognition framework, by transforming the underlying concept from token-passing to word-conditioned, and adding speedup methods like sparse LM look-ahead. On several different tasks, we achieve improvem...

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Bibliographic Details
Published in2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 3276 - 3280
Main Authors Nolden, David, Soltau, Hagen, Ney, Hermann
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2014
Subjects
Context
Decoding
dynamic
Hidden Markov models
Lattices
progress
Speech
Speech recognition
static
token-passing
Vocabulary
word conditioned
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Summary:We show how we boosted the efficiency of the dynamic network decoder in IBM's Attila speech recognition framework, by transforming the underlying concept from token-passing to word-conditioned, and adding speedup methods like sparse LM look-ahead. On several different tasks, we achieve improvements of 30 to 50% in efficiency at equal precision. We compare the efficiency to a state-of-the-art WFST based static decoder, and note that the added methods improve the dynamic decoder under conditions where it was lacking before in comparison, specifically when using a relatively small LM. Overall, the new dynamic decoder performs similarly to the static decoder, with a lead for the dynamic decoder on tasks with a larger LM, and a lead for the static decoder on tasks with a smaller LM.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2014.6854206