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，占多学科语音诊所的近一半病例。它可以对行为干预作出反应，但成功的治疗取决于适当的评估。目前的评估由于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，元音在不发音辅音前后的基频变化，通过元音更稳定部分的基频归一化）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估计耗时的手动性质的限制。本研究将为自动RFF估计的发展确定最佳的语音刺激和信号处理。我们将从运行语音和非语言语音中确定RFF估计的差异，语言语境对RFF和声音紧张之间关系的影响，以及语音障碍严重程度对自动RFF测量的影响。通过听者对声音张力的感知来估计健康和紊乱声音的声音张力，并通过声压级与声门下压力（dB SPL / cm H2O）的客观测量来交叉验证健康说话者的声音张力。使用非语言语音估计的RFF测量具有更可靠的自动化潜力。基于我们的实证研究，我们将为RFF收集的临床方法提出建议，以优化自动RFF估计：完全运行或非语言语音。我们将进一步开发用于自动RFF估计的开源算法和软件。自动化RFF估计将实现全面的临床收集，促进这一有前途的措施的未来验证。

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.