Automation of Relative Fundamental Frequency Estimation

描述(由申请人提供)：声带功能亢进(VH)，其特征是喉部过度紧张，占转诊到多学科嗓音诊所的病例的近一半。它可以对行为干预有反应，但成功的治疗取决于适当的评估。目前的评估因缺乏检测其存在或严重程度的客观措施而受到阻碍。相对基频(RFF，清辅音前后元音基频的变化，在比较稳定的元音部分用基频归一化)可以客观地表征VH。然而，RFF估计目前是由训练有素的技术人员和临床医生通过运行语音手动执行的，因此RFF在诊断和评估中的潜力受到RFF估计的耗时的手动性质的限制。这项研究将确定最优的语音刺激和信号处理，以发展自动RFF估计。我们将从连续语音和非语言语音话语中确定RFF估计的差异，语言环境对RFF和声音紧张之间关系的影响，以及发音困难严重程度对自动RFF测量的影响。将使用听者对声带紧张的感知来评估健康和紊乱嗓音的声带张力，并使用客观测量声压水平与声门下压力的比率(dBSPL/cm H2O)来交叉验证健康说话者的声带张力。使用非语言语音话语估计的RFF的测量具有更可靠地自动化的潜力。基于我们的实证研究，我们将为临床收集RFF的方法提出建议，以优化自动RFF估计：全语音或非语言语音。我们将进一步开发用于自动估计RFF的开源算法和软件。自动RFF估计将使全面的临床收集成为可能，促进这一有希望的措施的未来验证。

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.