Indirect Model-Based Speech Enhancement

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Model-based speech enhancement methods, such as vector-Taylor series-based methods (VTS) [1, 2], share a common methodology: they estimate speech using the expected value of the clean speech given the noisy speech under a statistical model. We show that it may be better to use the expected value of the noise under the model and subtract it from the noisy observation to form an indirect estimate of the speech. Interestingly, for VTS, this methodology turns out to be related to the application of an SNR-dependent gain to the direct VTS speech estimate. In results obtained on an automotive noise task, this methodology produces an average improvement of 1.6 dB signal-to-noise ratio (SNR), relative to conventional methods.