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使用多维语音程序、Praat 和 TF32 对健康和失调声音进行比较。

A Comparison of Healthy and Disordered Voices Using Multi-Dimensional Voice Program, Praat, and TF32.

机构信息

Department of Communication Disorders, University of Massachusetts Amherst, Amherst, Massachusetts.

Department of Communication Disorders, University of Massachusetts Amherst, Amherst, Massachusetts.

出版信息

J Voice. 2024 Jul;38(4):963.e23-963.e38. doi: 10.1016/j.jvoice.2022.01.010. Epub 2022 Mar 1.

Abstract

PURPOSE

Instrumental voice assessment plays a critical role in identifying vocal issues and for documenting treatment outcomes. The reported voice data, however, are sensitive to the algorithm used by each acoustic analysis software program (AASP) to analyze the corresponding waveform. In the present study, five acoustic measures were compared across healthy speakers and speakers with dysphonia for three AASPs commonly used in research, education, and clinical practice: Multidimensional Voice Program (MDVP) by Computerized Speech Lab, Praat, and TF32.

MATERIALS AND METHODS

Sustained vowel phonations for the quantal vowels /ɑ/, /i/, and /u/ were analyzed for 80 speakers with organic dysphonia and 60 age- and sex-matched healthy controls. Descriptive, inferential, and correlation data are reported for mean fundamental frequency (mean F0), standard deviation of fundamental frequency (SD F0), short-term perturbation measures of jitter and shimmer, and harmonic-to-noise ratio (HNR).

RESULTS

The present study replicated previous findings of interprogram differences for healthy speakers, with MDVP consistently yielding higher values than Praat and TF32 for SD F0, jitter, and shimmer and lower values for HNR. Similar, but magnified patterns of results were observed for speakers with dysphonia.

CONCLUSION

The variation observed across programs calls into question the validity in comparing voice outcomes reported by one AASP to those previously obtained by another, particularly for acoustic signals with aperiodic components that are commonly present in disordered voices. It is advised that waveforms be visually inspected prior to conducting acoustic analysis, and that voice outcomes not be combined or compared across AASPs.

摘要

目的

仪器语音评估在识别语音问题和记录治疗效果方面起着至关重要的作用。然而,报告的语音数据对每个声学分析软件程序(AASP)用于分析相应的波形的算法敏感。在本研究中,比较了三种在研究、教育和临床实践中常用的 AASPs(计算机语音实验室的多维语音计划(MDVP)、Praat 和 TF32)在健康说话者和发音障碍说话者中的五个声学测量值。

材料和方法

对 80 名患有器质性发音障碍的发音者和 60 名年龄和性别匹配的健康对照者进行了持续元音发音的分析,所用的音为定量元音 /ɑ/、/i/和/u/。报告了均值基频(mean F0)、基频标准差(SD F0)、短期微扰测量值抖动和振幅微扰(shimmer)以及谐波噪声比(HNR)的描述性、推论性和相关性数据。

结果

本研究复制了先前在健康说话者中发现的程序间差异的发现,MDVP 始终比 Praat 和 TF32 产生更高的 SD F0、抖动和振幅微扰值,而 HNR 值更低。在发音障碍的说话者中也观察到了类似但放大的结果模式。

结论

观察到的程序间差异质疑了将一个 AASP 报告的语音结果与另一个 AASP 先前获得的结果进行比较的有效性,特别是对于具有非周期性成分的声学信号,这些成分通常存在于紊乱的声音中。建议在进行声学分析之前先对波形进行目视检查,并且不要在 AASPs 之间组合或比较语音结果。

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