Search Contact information
University of Cambridge Home Department of Engineering
University of Cambridge > Engineering Department > Machine Intelligence Lab

Abstract for logan_icassp97

Proc. ICASSP '97

ENHANCEMENT AND RECOGNITION OF NOISY SPEECH WITHIN AN AUTOREGRESSIVE HIDDEN-MARKOV-MODEL FRAMEWORK USING NOISE ESTIMATES FROM THE NOISY SIGNAL

B. T. Logan and A. J. Robinson

April 1997

This paper describes a new algorithm to enhance and recognise noisy speech when only the noisy signal is available. The system uses autoregressive hidden Markov models (HMMs) to model the clean speech and noise and combines these to form a model for the noisy speech. The probability framework developed is then used to reestimate the noise models from the corrupted speech waveform and the process is repeated. Enhancement is performed using the Wiener filters formed from the final clean speech models and noise estimates. Results are presented for additive stationary Gaussian and coloured noise.


(ftp:) logan_icassp97.ps.Z (http:) logan_icassp97.ps.Z
PDF (automatically generated from original PostScript document - may be badly aliased on screen):
  (ftp:) logan_icassp97.pdf | (http:) logan_icassp97.pdf

If you have difficulty viewing files that end '.gz', which are gzip compressed, then you may be able to find tools to uncompress them at the gzip web site.

If you have difficulty viewing files that are in PostScript, (ending '.ps' or '.ps.gz'), then you may be able to find tools to view them at the gsview web site.

We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.

© 2005 Cambridge University Engineering Dept
Information provided by milab-maintainer