Unsupervised training of a speech recognizer: recent experiments
Thomas Kemp, Alex Waibel · 1999
Current speech recognition systems require large amounts of transcribed data for parameter estimation. The transcription, however, is tedious and expensive. In this work we describe our experiments which are aimed at training a speech recognizer with only a minimal amount (30 minutes) of transcriptions and a large portion (50 hours) of untranscribed data. A recognizer is bootstrapped on the transcribed part of the data and initial transcripts are generated with it for the remainder (the untranscribed part). Using a lattice-based confidence measure, the recognition errors are (partially) detected and the remainder of the hypotheses is used for training. Using this scheme, the word error rate on a broadcast news speech recognition task dropped from more than 32.0% to 21.4%. In a cheating experiment we show, that this performance cannot be significantly improved by improving the measure of confidence. By combining the unsupervisedly trained system with our currently best recognizer which ...