OBJECTIVE/HYPOTHESIS: Acoustic analysis is a commonly used method for quantitatively measuring vocal fold function. Voice signals are analyzed by selecting a waveform segment and using various algorithms to arrive at parameters such as jitter, shimmer, and signal-to-noise ratio (SNR). Accurate and reliable methods for selecting a representative vowel segment have not been established. STUDY DESIGN: Prospective repeated-measure experiment. METHODS: We applied a moving window method by isolating consecutive, overlapping segments of the raw voice signal from onset through offset. Ten normal voice signals were analyzed using acoustic measures calculated from the moving window. The location and value of minimum perturbation/maximum SNR was compared across individuals. The moving window method was compared with data from the whole vowel excluding onset and offset, the mid-vowel, and the visually selected steadiest portion of the voice signal. RESULTS: Results showed that the steadiest portion of the waveforms, as defined by minimum perturbation and maximum SNR values, was not consistent across individuals. Perturbation and nonlinear dynamic values differed significantly based on what segment of the waveform was used. Other commonly used segment selection methods resulted in significantly higher perturbation values and significantly lower SNR values than those determined by the moving window method (P<0.001). CONCLUSIONS: The selection of a sample for acoustic analysis can introduce significant inconsistencies into the analysis procedure. The moving window technique may provide more accurate and reliable acoustic measures by objectively identifying the steadiest segment of the voice sample.
OBJECTIVE/HYPOTHESIS: Acoustic analysis is a commonly used method for quantitatively measuring vocal fold function. Voice signals are analyzed by selecting a waveform segment and using various algorithms to arrive at parameters such as jitter, shimmer, and signal-to-noise ratio (SNR). Accurate and reliable methods for selecting a representative vowel segment have not been established. STUDY DESIGN: Prospective repeated-measure experiment. METHODS: We applied a moving window method by isolating consecutive, overlapping segments of the raw voice signal from onset through offset. Ten normal voice signals were analyzed using acoustic measures calculated from the moving window. The location and value of minimum perturbation/maximum SNR was compared across individuals. The moving window method was compared with data from the whole vowel excluding onset and offset, the mid-vowel, and the visually selected steadiest portion of the voice signal. RESULTS: Results showed that the steadiest portion of the waveforms, as defined by minimum perturbation and maximum SNR values, was not consistent across individuals. Perturbation and nonlinear dynamic values differed significantly based on what segment of the waveform was used. Other commonly used segment selection methods resulted in significantly higher perturbation values and significantly lower SNR values than those determined by the moving window method (P<0.001). CONCLUSIONS: The selection of a sample for acoustic analysis can introduce significant inconsistencies into the analysis procedure. The moving window technique may provide more accurate and reliable acoustic measures by objectively identifying the steadiest segment of the voice sample.