Robust estimation of speech in noisy backgrounds based on aspects of the auditory process

A new approach to speech enhancement is proposed where constraints based on aspects of the auditory process augment an iterative enhancement framework. The basic enhancement framework is based on a previously developed dual-channel scenario using a two-step iterative Wiener filtering algorithm. Constraints across broad speech sections and over iterations are then experimentally developed on a novel auditory representation derived by transforming the speech magnitude spectrum. The spectral transformations are based on modeling aspects of the human auditory process which include critical band filtering, intensity-to-loudness conversion, and lateral inhibition. The auditory transformations and perceptual based constraints are shown to result in a new set of auditory constrained and enhanced linear prediction (ACE-LP) parameters. The ACE-LP based speech spectrum is then incorporated into the iterative Wiener filtering framework. The improvements due to auditory constraints are demonstrated in several areas. The proposed auditory representation is shown to result in improved spectral characterization in background noise. The auditory constrained iterative enhancement (ACE-II) algorithm is shown to result in improved quality over all sections of enhanced speech. Adaptation of auditory based constraints to changing spectral characteristics over broad classes of speech is another novel aspect of the proposed algorithm. The consistency of speech quality improvement for the ACE-II algorithm is illustrated over time and across all phonemes classified over a large set of phonetically balanced sentences from the TIMIT database. This study demonstrates the application of auditory based perceptual properties of a human listener to speech enhancement in noise, resulting in improved and consistent speech quality over all regions of speech.