Improving Acoustic Models by Watching Television

Intelligent Integration and Use of Text, Image, Video, and Audio Corpora

Volume

Papers from the 1997 AAAI Spring Symposium

Contents

Obtaining sufficient labelled training data is a persistent difficulty for speech recognition research. Although well transcribed data is expensive to produce, there is a constant stream of challenging speech data and poor transcription broadcast as closed-captioned television. We describe a reliable unsupervised method for identifying accurately transcribed sections of these broadcasts, and show how these segments can be used to train a recognition system. Starting from acoustic models trained on the Wall Street Journal database, a single iteration of our training method reduced the word error rate on an independent broadcast television news test set from 62.2 % to 59.5%.