Abstract

Typical speech enhancement algorithms operate on the short-time magnitude spectrum, while keeping the short-time phase spectrum unchanged for synthesis. We propose a novel approach where the noisy magnitude spectrum is recombined with a changed phase spectrum to produce a modified complex spectrum. During synthesis, the low energy components of the modified complex spectrum cancel out more than the high energy components, thus reducing background noise. Using objective speech quality measures, informal subjective listening tests and spectrogram analysis, we show that the proposed method results in improved speech quality.

No abstract

A comparative evaluation of speech enhancement algorithms for robust automatic speech recognition is presented. The evaluation is performed on a core test set of the TIMIT speech corpus. Mean objective speech quality scores as well as ASR correctness scores under two noise conditions are given.

In this paper, we propose to combine the Kalman filter with a recent speech enhancement technique, called the phase spectrum compensation procedure, or PSC. More specifically, we apply the PSC technique to initialise the Kalman filter, whereby PSC is used to clean the noisy speech prior to LPC estimation for the Kalman recursion. We refer to the combined technique as the Kalman-PSC filter. Using an objective speech quality measure, formal subjective listening tests and spectrogram analysis, we show that the proposed method results in improved speech quality.