Neural networks have proven their ability to be usefully applied as component of a speech enhancement system. This is based on the known feature of neural nets to map regions inside a feature space to other regions. It can be taken to map noisy magnitude spectra to clean spectra. This way the net can be used to substitute an adaptive filtering in the spectral domain. We set up such a system and compared its performance against a known adaptive filtering approach in terms of speech quality and in terms of recognition rate. It is a still not fully answered question how far the speech quality can be enhanced by modifying not only the magnitude but also the spectral phase and how this phase modification could be realized. Before trying to use a neural network for a possible modification of the phase spectrum we ran a set of oracle experiments to find out how far the quality can be improved by modifying the magnitude and/or the phase spectrum in voiced segments. It turns out that the simultaneous modification of magnitude and phase spectrum has the potential for a considerable improvement of the speech quality in comparison to modifying the magnitude or the phase only.
Cite as: Hirsch, H.-G., Gref, M. (2017) On the Influence of Modifying Magnitude and Phase Spectrum to Enhance Noisy Speech Signals. Proc. Interspeech 2017, 1978-1982, doi: 10.21437/Interspeech.2017-1173
@inproceedings{hirsch17_interspeech,
author={Hans-Günter Hirsch and Michael Gref},
title={{On the Influence of Modifying Magnitude and Phase Spectrum to Enhance Noisy Speech Signals}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={1978--1982},
doi={10.21437/Interspeech.2017-1173}
}