- T. Kemp, A. Waibel. 1999. Unsupervised training of a speech recognizer: recent experiments. Abstract: 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 un-transcribed 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 con(cid:12)dence 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 signi(cid:12)cantly improved by improving the measure of con(cid:12)dence. By combining the unsupervisedly trained system with our currently best recognizer which is trained on 15.5 hours of transcribed data, an additional error reduction of 5% relative (as compared to the system trained in a standard fashion) is possible.
