Measuring Breathiness with an Auditory Processing Model

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DESCRIPTION (provided by applicant): Measurement of voice quality is important for making several decisions regarding the management of patients with voice disorders. It has been shown that subjective ratings of voice quality made in a routine clinical set-up are highly inconsistent, and may not be valid measures of voice quality. An alternative approach to measure voice quality is to develop acoustic measures that closely correspond with listeners' perception of voice quality. However, none of the measures developed thus far show a high and consistent correlation with listener's perception of the vocal quality under study. The poor and inconsistent correlation between acoustic measures and perceptual ratings of voice quality may arise from the non-linear and multidimensional relationship between the acoustic signal and its perceptual correlate. Non-linear relationships between a physical stimulus and its psychophysical correlate are seen in all human sensory systems. For example, in the auditory domain the relation between intensity and loudness is better explained by the power law and that between frequency and pitch is better mapped using the Mel or the Bark scale. Recent advances in psychoacoustics and auditory physiology has led to the development of several auditory models which are capable of accounting for such non-linear phenomena. Because these non-linear processes are inherent to all auditory perceptual tasks, these also affect how a specific vocal acoustic signal affects the perception of voice quality. This research will investigate the use of an auditory processing model as a signal processing front end to study voice quality. It is hypothesized that the output of such an auditory model will provide objective measures of voice quality that show a high and consistent correlation with perceptual ratings of voice quality. The proposed research seeks to (1) study the effects of specific changes in vocal acoustic signal on the perception of breathy voice quality, (2) use an auditory processing model to understand what changes in the auditory spectrum lead to the changes in perceived voice quality, and (3) develop objective measures to explain perceptual data in synthetic and natural vowels.