Automation of Relative Fundamental Frequency Estimation

相对基频估计的自动化

• 批准号: 8487636
• 负责人: Cara E. Stepp
• 金额: $ 15.35万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-05 至 2016-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8487636
• 关键词: Accounting; Acoustics; Algorithms; Automation; Behavior Therapy; Clinic; Clinical; Collection; Communication; Computer software; Development; Diagnosis; Diagnostic Sensitivity; Dysphonia; Empirical Research; Female; Frequencies; Future; Individual; Larynx; Linguistics; Manuals; Measurement; Measures; Methodology; Methods; Multi-Institutional Clinical Trial; Nature; Patients; Perception; Physiological; Population; Production; Protocols documentation; Recommendation; Relative (related person); Research; Running; Sampling; Science; Severities; Speech; Stimulus; Techniques; Testing; Time; Training; Validation; Voice; Voice Disorders; Woman; Work; base; clinical care; computerized data processing; design; improved; male; multidisciplinary; open source; pressure; public health relevance; research study; sound; voice therapy

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.

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。目前的评估因缺乏客观的措施来检测其存在或严重程度而受到阻碍。相对基本频率（RFF，清音辅音之前和之后元音基本频率的变化，由更稳定状态部分元音的基本频率标准化）可以客观地表征VH。然而，RFF估计目前由受过训练的技术人员和临床医生根据运行语音手动执行，使得RFF在诊断和评估中的潜力受到RFF估计的耗时手动性质的限制。这项研究将确定最佳的语音刺激和信号处理的自动RFF估计的发展。我们将确定的差异RFF估计从运行的语音与非语言的语音话语，语言环境的影响RFF和声乐紧张之间的关系，以及发音困难的严重程度的影响自动RFF措施。将使用听者对声音张力的感知来估计健康和紊乱声音中的声音张力，并使用声压级与声门下压力（dB SPL / cm H2O）的比率的客观测量在健康说话者中进行交叉验证。使用非语言的语音话语估计RFF的措施有可能被自动化更可靠。根据我们的实证研究，我们将制定建议的RFF收集的临床方法，以优化自动RFF估计：完整的运行或非语言语音。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RFF估计将实现全面的临床收集，促进未来验证这一有前途的措施。