Probability-Based Best Sample Selection for Acoustic Analysis of Normal and Disordered Voices.
Boquan Liu, Jacob F Reiss, Jack J Jiang
PMID 32482492WHAT IT FOUND
A new method for selecting stable voice segments produced lower complexity scores and higher signal-to-noise ratios than three standard methods in both normal and disordered voices.
This suggests it isolates the most stable part of a voice sample more reliably for acoustic analysis.
Key findings
01The modal periodogram method yielded significantly lower D2 values and higher SCR values than whole vowel, mid-vowel, and moving window methods for both normal and disordered voices.
02Jitter was not suitable for differentiating between normal and disordered voices in this study, unlike D2 and SCR which showed significant differences.
STILL TO COME
How it was doneWhat they foundWhat it means for SLPs
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What it does not show
The study only included adult subjects, so it does not address whether this method works for pediatric voices. The analysis excluded jitter for disordered voices, limiting the scope of parameters where this method's advantage was demonstrated. The study compared methods on existing database samples, not in a live clinical setting, so practical implementation challenges were not assessed.
Declared interests
The authors declared no conflicts of interest.
The easy way to misread this
Do not assume this method improves diagnostic accuracy for voice disorders. It only demonstrates better stability in the selected audio segment; it does not prove that acoustic analysis using this method leads to better clinical outcomes or diagnosis compared to standard practices.