Recent advances in efficient decoding combining on-line transducer composition and smoothed language model incorporation
Daniel Willett, Shigeru Katagiri · IEEE International Conference on Acoustics Speech and Signal Processing · 2002
This paper presents and evaluates our recent efforts on efficient decoding for Large Vocabulary Continuous Speech Recognition in the framework of Weighted Finite State Transducers. We evaluate on-the-fly transducer composition for reduced memory consumption combined with weight smearing for a more time-synchronous language model incorporation. It turns out that in the on-line composition mode weight smoothing within the static part of the network is even more beneficial on run-time to accuracy ratio than in the fully precompiled case. Evaluations are carried out on a state-of-the-art recognition system of 10k words, cross-word triphone acoustic models and trigram language model. In this scenario, the Viterbi-search is carried out fully time-synchronously in only a single pass. The combination of on-the-fly network composition with only the unigram part of the language model smoothly compiled into the network achieves a remarkably good run-time to accuracy ratio with only moderate memory requirements.