  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.
