Spontaneous dialogue speech recognition using cross-word context constrained word graphs

This paper proposes a large vocabulary spontaneous dialogue speech recognizer using cross-word context constrained word graphs. In this method, two approximation methods 'cross-word context approximation' and 'lenient language score smearing' are introduced to reduce the computational cost for word graph generation. The experimental results using a 'travel arrangement corpus' show that this recognition method achieves a word hypotheses reduction of 25-40% and a cpu-time reduction of 30-60% compared to without approximation, and that the use of class bigram scores as the expected language score for each lexicon tree node decreases the word error rate 25-30% compared to without approximation.