Sixth International Conference on Spoken Language Processing
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)  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 con- fidence measures improve the adaptation performance. We present experimental results on the VERBMOBIL task, a German conversational speech corpus.
Bibliographic reference. Pitz, Michael / Wessel, Frank / Ney, Hermann (2000): "Improved MLLR speaker adaptation using confidence measures for conversational speech recognition", In ICSLP-2000, vol.4, 548-551.