Example-based speech recognition using formulaic phrases

In this paper, we describe the design of an ASR system that is based on identifying and extracting formulaic phrases from a corpus and then, rather than building statistical models of them, performing example-based recognition of these phrases. We describe a method for combining formulaic phrases into a bigram language model that results in a 13% decrease in WER on a monophone HMM recogniser over the baseline. We show that using this model with phrase templates in the example-based recogniser gives a significant improvement in WER compared to word templates, but performance still falls short of the HMM recogniser. We also describe an LDA decision tree classifier that reduces the search space of the DTW decoder by 40% while at the same time decreasing WER.