CONSTRAINED ITERATIVE SPEECH ENHANCEMENT WITH APPLICATION TO SPEECH RECOGNITION

In this paper, an improved form of iterative speech enhancement for single channel inputs is formulated. The basis of the procedure is sequential maximum a posteriori estimation of the speech waveform and its all-pole parameters as originally formulated by Lim and Oppenheim, followed by imposition of constraints upon the sequence of speech spectra. The new approaches impose intraframe and interframe constaints on the input speech signal to ensure more speech-like formant trajectories, reduce frame-to-frame pole jitter, and effectively introduce a relaxation parameter to the iterative scheme. Recently discovered properties of the line spectral pair representation of speech allow for an efficient and direct procedure for application of many of the constraint requirements. Substantial improvement over the unconstrained method has been observed in a variety of domains. First, informal listener quality evaluation tests and objective speech quality measures demonstrate the technique's effectiveness for additive white Gaussian noise. A consistent terminating point for the iterative technique is also shown. Second, the algorithms have been generalized and successfully tested for noise which is nonwhite and slowly varying in characteristics. The current systems result in substantially improved speech quality and LPC parameter estimation in this context with only a minor increase in computational requirements. Third, the algorithms were evaluated with respect to improving automatic recognition of speech in the presence of additive noise, and shown to out-perform other enhancement methods in this application.