Voice disorders often lead to changes in voice quality noticed by patients, clinicians, and conversation partners, and improvement in voice quality is a critical outcome of treatment. However, we have limited knowledge of how people perceive voice quality. This has restricted our ability to accurately quantify or describe changes in qualit, such as due to a disease or when resulting from treatment. This continuation project combines concepts and techniques from voice science, speech science, hearing science, and engineering to address this problem. In general, the research proceeds by first obtaining high-precision measures of voice quality perception in the laboratory. These data are then used to develop mathematical models of voice quality perception that accurately reflect listeners'data. To obtain a close match between human judgments of voice quality and model output, models of auditory processing are used to obtain an internal representation of the voice acoustic signal. Specific measures are then captured from this internal auditory representation and used to model the perception of voice quality. Methods for obtaining perceptual judgments of single voice quality dimensions, the transformation of the acoustic signal to its internal representation, and the general form of the voice quality models have been completed for two different voice quality dimensions (breathiness and roughness) using simple stimuli (vowel /a/ as in "hot"). In the proposed work, these approaches will be developed further to establish a framework for comprehensive understanding of voice quality perception and to enable translation to clinical practice. These approaches will be (1) used to account for multiple, co-occurring voice quality dimensions;(2) applied to more natural and complex stimuli (multiple vowels and syllables);and (3) leveraged to understand other voice quality dimensions (strain). (4) To increase model accuracy and to expand their applicability to severely dysphonic voices (e.g. Type II and Type III), methods to estimate the "pitch" and "pitch strength" of dysphonic voices will be developed and incorporated into relevant models. (4) To enhance the measurement schemes in a manner that improves clinical utility, model output will be transformed to a scale that is intuitively relted to the perceptual magnitude of each voice quality dimension. This will create a set of intuitive voice quality metrics that are easy to use and interpret. (5) Finally, the feasibility of using thee models and metrics in regular clinical assessment will be evaluated through an initial clinical study.

Public Health Relevance

The goal of this work is to revolutionize the use of dysphonic voice quality measurement for clinical practice and research by establishing new psychometric scales and developing highly precise, reliable and automated estimates of voice quality based on quantitative models of auditory perceptual behavior.

National Institute of Health (NIH)
Research Project (R01)
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Motor Function, Speech and Rehabilitation Study Section (MFSR)
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Shekim, Lana O
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Michigan State University
Other Health Professions
Schools of Arts and Sciences
East Lansing
United States
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Eddins, David A; Shrivastav, Rahul (2013) Psychometric properties associated with perceived vocal roughness using a matching task. J Acoust Soc Am 134:EL294-300
Patel, Sona; Shrivastav, Rahul; Eddins, David A (2012) Developing a single comparison stimulus for matching breathy voice quality. J Speech Lang Hear Res 55:639-47
Shrivastav, Rahul; Eddins, David A; Anand, Supraja (2012) Pitch strength of normal and dysphonic voices. J Acoust Soc Am 131:2261-9
Shrivastav, Rahul; Camacho, Arturo; Patel, Sona et al. (2011) A model for the prediction of breathiness in vowels. J Acoust Soc Am 129:1605-15
Shrivastav, Rahul; Camacho, Arturo (2010) A computational model to predict changes in breathiness resulting from variations in aspiration noise level. J Voice 24:395-405
Patel, Sona; Shrivastav, Rahul; Eddins, David A (2010) Perceptual distances of breathy voice quality: a comparison of psychophysical methods. J Voice 24:168-77