Blind Normalization of Speech From Different Channels

TL;DR

This paper introduces a blind normalization technique for speech signals that removes channel effects by non-linearly rescaling cepstral features, improving channel invariance without needing noise measurements.

Contribution

The proposed method provides a novel, channel-independent speech representation by blindly rescaling cepstra, outperforming traditional normalization methods without requiring noise or reverberation measurements.

Findings

Achieved greater channel-independence than cepstral mean normalization

Comparable to combined normalization and spectral subtraction methods

No measurements of channel noise or reverberations needed

Abstract

We show how to construct a channel-independent representation of speech that has propagated through a noisy reverberant channel. This is done by blindly rescaling the cepstral time series by a non-linear function, with the form of this scale function being determined by previously encountered cepstra from that channel. The rescaled form of the time series is an invariant property of it in the following sense: it is unaffected if the time series is transformed by any time-independent invertible distortion. Because a linear channel with stationary noise and impulse response transforms cepstra in this way, the new technique can be used to remove the channel dependence of a cepstral time series. In experiments, the method achieved greater channel-independence than cepstral mean normalization, and it was comparable to the combination of cepstral mean normalization and spectral subtraction, despite the fact that no measurements of channel noise or reverberations were required (unlike spectral subtraction).