Dual-channel iterative speech enhancement with constraints on an auditory-based spectrum

A new frequency-domain, constrained iterative algorithm is proposed for dual-channel speech enhancement. The dual-channel enhancement scheme is shown to follow the iterative expectation-maximization (EM) algorithm, resulting in a two-step dual-channel Wiener filtering scheme. A new technique for applying constraints during the EM iterations is developed so as to take advantage of the auditory properties of speech perception. An overriding goal is to enhance quality and at least maintain intelligibility of the estimated speech signal. Constraints are applied over time and iteration on mel-cepstral parameters which parametrize an auditory based spectrum. These constraints also adapt to changing speech characteristics over time with the aid of an adaptive boundary detector. Performance is demonstrated in three areas for speech degraded by additive white Gaussian noise, aircraft cockpit noise, and computer cooling-fan noise. First, global objective speech quality measures show improved quality when compared to unconstrained dual-channel Wiener filtering and a traditional LMS-based adaptive noise cancellation technique, over a range of signal-to-noise ratios and cross-talk levels. Second, time waveforms and frame-to-frame quality measures show good improvement, especially in unvoiced and transitional regions of speech. Informal listening tests confirm improvement in duality as measured by objective measures. Finally, objective measures classified over individual phonemes for a subset of sentences from the TIMIT speech database show a consistent and superior improvement in quality.<
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