On-line incremental speaker adaptation with automatic speaker change detection

  • Authors:

  • Zhi-Peng Zhang;S. Furui;K. Ohtsuki

  • Affiliations:

  • Dept. of Comput. Sci., Tokyo Inst. of Technol., Japan;-;-

  • Venue:
  • ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
  • Year:
  • 2000

Quantified Score

Hi-index 0.00

Visualization

Abstract

In order to improve the performance of speech recognition systems when speakers change frequently and each of them utters a series of several sentences, a new unsupervised, online and incremental speaker adaptation technique combined with automatic detection of speaker changes is proposed. The speaker change is detected by comparing likelihoods using speaker-independent and speaker-adaptive Gaussian mixture models (GMMs). Both the phone HMM and GMM are adapted by MLLR transformation. In a broadcast news transcription task, this method reduces the word error rate by 10.0%. In comparison with the conventional method that uses HMMs for the speaker change detection, the GMM-based method requires a significantly less number of computations at the cost of only a slightly lower word recognition rate.