Recent advances in efficient decoding combining on-line transducer composition and smoothed language model incorporation

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.