Automatic speech recognition using acoustic confidence conditioned language models
Richard Cameron Rose, Giuseppe Riccardi · 1999
A modi ed decoding algorithm for automatic speech recognition ASR will be described which facilitates a closer coupling between the acoustic and language modeling components of a speech recognition system.This closer coupling is obtained by extracting word level measures of acoustic con dence during decoding, and making coded representations of these con dence measures available to the ASR network during decoding.A simulation of this decoding strategy is implemented using a word lattice rescoring paradigm.A joint acoustic language model will be described where linguistic context is augmented to include the encoded values of acoustic con dence.Finally, the performance of the word lattice based implementation of the decoding algorithm will be evaluated on a large vocabulary natural language understanding task.