Phase-based Information for Voice Pathology Detection

TL;DR

This paper explores the use of phase-based features, particularly group delay functions, for voice disorder detection, demonstrating their high discriminative power and complementarity to magnitude spectrum features.

Contribution

It introduces phase-based features for voice pathology detection and compares their effectiveness to traditional magnitude spectrum features, highlighting their complementary nature.

Findings

Phase-based features effectively characterize phonation irregularities.

They provide high discrimination performance in voice disorder detection.

Phase features complement magnitude spectrum features.

Abstract

In most current approaches of speech processing, information is extracted from the magnitude spectrum. However recent perceptual studies have underlined the importance of the phase component. The goal of this paper is to investigate the potential of using phase-based features for automatically detecting voice disorders. It is shown that group delay functions are appropriate for characterizing irregularities in the phonation. Besides the respect of the mixed-phase model of speech is discussed. The proposed phase-based features are evaluated and compared to other parameters derived from the magnitude spectrum. Both streams are shown to be interestingly complementary. Furthermore phase-based features turn out to convey a great amount of relevant information, leading to high discrimination performance.