ISCA Archive ICSLP 2000
ISCA Archive ICSLP 2000

Improved MLLR speaker adaptation using confidence measures for conversational speech recognition

Michael Pitz, Frank Wessel, Hermann Ney

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 con- fidence measures improve the adaptation performance. We present experimental results on the VERBMOBIL task, a German conversational speech corpus.

C.J. Leggetter, P.C.Woodland: "Maximum Likelihood linear regression for speaker adaptation of continuous density hidden Markov models", Computer, Speech and Language, vol. 9, pp. 171-185, 1995.


Cite as: Pitz, M., Wessel, F., Ney, H. (2000) Improved MLLR speaker adaptation using confidence measures for conversational speech recognition. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 4, 548-551

@inproceedings{pitz00_icslp,
  author={Michael Pitz and Frank Wessel and Hermann Ney},
  title={{Improved MLLR speaker adaptation using confidence measures for conversational speech recognition}},
  year=2000,
  booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
  pages={vol. 4, 548-551}
}