Microphone-array speech recognition via incremental map training

  • Authors:

  • J. E. Adcock;Y. Gotoh;D. j. Mashao;H. F. Silverman

  • Affiliations:

  • LEMS, Brown Univ., Providence, RI, USA;-;-;-

  • Venue:
  • ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
  • Year:
  • 1996

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Abstract

For a hidden Markov model (HMM) based speech recognition system it is desirable to combine enhancement of the acoustical signal and statistical representation of model parameters, ensuring both a high quality speech signal and an appropriately trained HMM. In this paper the incremental variant of maximum a posteriori (MAP) estimation is used to adjust the parameters of a talker-independent HMM-based speech recognition system to accurately recognize speech data acquired with a microphone-array. The approach is novel for a microphone-array speech recognition task in that a robust talker-independent model is derived from a baseline system using a relatively small amount of data for training. The results show that (1) ILIAP training significantly improves recognition performance compared to the baseline, and (2) beamforming signal enhancement outperforms single-channel enhancement before and after the adaptive MAP training.