Improved MLLR speaker adaptation using confidence measures for conversational speech recognition
Michael Pitz, Frank Wessel, Hermann Ney · 2000
Automatic recognition of conversational speech tends to have higher word error rates (WER) than read speech. Improvements gained from unsupervised speaker adaptation methods like Maximum Likelihood Linear Regression (MLLR) [1] are reduced because of their sensitivity to recognition errors in the first pass. We show that a more detailed modeling of adaptation classes and the use of confidence measures improve the adaptation performance. We present experimental results on the VERBMOBIL task, a German conversational speech corpus.