Automation of Relative Fundamental Frequency Estimation

Boston University (Charles River Campus), Boston MA

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Abstract

DESCRIPTION (provided by applicant): Vocal hyperfunction (VH), which is characterized by excessive laryngeal tension, accounts for nearly half of the cases referred to multidisciplinary voice clinics and. It can respond to behavioral intervention, but successful treatment depends on proper assessment. Current assessment is hampered by the lack of objective measures for detecting its presence or severity. Relative Fundamental Frequency (RFF, the change in fundamental frequency in vowels preceding and following unvoiced consonants, normalized by fundamental frequency in the more steady state portions vowels) can objectively characterize VH. However, RFF estimates are currently performed manually by trained technicians and clinicians from running speech such that the potential of RFF in diagnosis and assessment is limited by the time-consuming manual nature of RFF estimation. This study will determine the optimal speech stimuli and signal processing for development of automated RFF estimation. We will determine the differences in RFF estimates from running speech versus non-linguistic speech utterances, the effect of linguistic context on the relationship between RFF and vocal tension, and the impact of dysphonia severity on the automated RFF measure. Vocal tension will be estimated in healthy and disordered voices using listener perception of vocal strain, and cross-validated in healthy speakers using objective measurements of the ratio of sound pressure level to subglottal pressure (dB SPL / cm H2O). Measures of RFF estimated using non-linguistic speech utterances have the potential to be automated more reliably. Based on our empirical research, we will develop recommendations for clinical methods of RFF collection to optimize automatic RFF estimation: full running or non-linguistic speech. We will further develop open source algorithms and software for automated RFF estimation. Automated RFF estimation would enable comprehensive clinical collection, facilitating future validation of this promising measure.