Enhancement of electrolarynx speech using adaptive noise cancelling based on independent component analysis

Abstract

The electrolarynx provides a valuable means of verbal communication for people who cannot use their natural voice-production mechanism, but technology has changed very little since it was introduced in the 1950s. The presence of background noise degrades the resulting speech. In this study background noise was reduced by a new method, independent component analysis-based adaptive noise cancelling, which can remove noise components of the primary input signal based on statistical independence, by incorporating both second-order and higher-order statistics. The method shows better performance than the conventional least mean square algorithm. Acoustic analysis of the denoised electrolarynx speech revealed a significant reduction in the amount of background noise. Results from the perceptual evaluations indicated that the new filtering technique produced a noticeable improvement in the acceptability of the electrolarynx speech in a quiet environment (from 1.75 to 2.49, arbitrary units) or a noisy environment (from 0.59 to 1.82). In general, there was no significant improvement or degradation in intelligibility in the quiet environment (from 52.7 to 53.3). However, the processing did improve the intelligibility in a babble-noise environment (from 24.9 to 40.6). The improvement in acceptability and intelligibility may increase the communication ability of the user in daily situations.