SPEECH ENHANCEMENT USING A-PRIORI INFORMATION WITH CLASSIFIED NOISE CODEBOOKS

This paper focuses on the estimation of short-term linear predictive parameters from noisy speech and their subsequent use in wave-form enhancement schemes. We use a-priori information in the form of trained codebooks of speech and noise linear predictive coefficients. The excitation variances of speech and noise are de-termined through the optimization of a criterion that finds the best fit between the noisy observation and the model represented by the two codebooks. Improved estimation accuracy and reduced com-putational complexity result from classifying the noise and using small noise codebooks, one for each noise class. For each segment of noisy speech, the classification scheme selects a particular noise codebook. Experimental results show good performance, especially under non-stationary noise conditions. Listening tests confirm that the new method outperforms conventional speech enhancement sys-tems. 1