Spectral analysis of the voice in Down Syndrome.
Spectral voice analysis picks up quiet acoustic quirks in adults with Down syndrome; kids sound too similar to controls for the measure to help.
01Research in Context
What this study did
Sharp et al. (2010) ran a lab study on voice quality. They recorded adults and children with Down syndrome. A computer program measured tiny sound-wave details called spectral moments.
The same people came back later so the team could check if the numbers stayed the same.
What they found
Adults with Down syndrome showed several clear spectral differences. Their voices had a different “sound print” than adults without the syndrome.
Kids with Down syndrome mostly sounded like other kids. Only one measure, the coefficient of variation, was different. Test-retest scores also bounced around more in both DS groups.
How this fits with other research
Fusaroli et al. (2017) pooled many autism studies and found small but real voice differences. Like Sharp et al. (2010), they warn the gaps are too small for a yes-or-no test.
Robinson et al. (2011) meta-analysis shows wide language and short-term memory gaps in Down syndrome. Voice spectra add a new piece: the raw sound is mildly altered in adults, even if words themselves are the main problem.
Doughty et al. (2010) used the same computer-acoustic trick in autism. Both papers prove cheap software can flag subtle sound quirks, but each syndrome tweaks a different part of the signal.
Why it matters
If you work with adults who have Down syndrome, expect their voices to carry slight spectral “fingerprints.” The tool is not ready for diagnosis, but it can track change after voice therapy. Skip it for young kids; the numbers overlap too much. Always pair voice data with language and memory tests shown by Robinson et al. (2011) for a full picture.
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02At a glance
03Original abstract
The voice quality of individuals with Down Syndrome (DS) is generally described as husky, monotonous and raucous. On the other hand, the voice of DS children is characterized by breathiness, roughness, and nasality and is typically low pitched. However, research on phonation and intonation in these participants is limited. The present study was designed to provide data from the spectral analysis of the human voice in DS people. A cross-sectional, observational design was applied. Thirty DS adults and 48 DS children were enrolled after clinical evaluation. Thirty men, 30 women and 46 children constituted the control group. The participants had to repeat a set of Italian words twice. The Real Time Pitch software manufactured by KayPENTAX recorded the voice. The following spectral descriptors were obtained for each word: Mean Frequency and standard deviation, Energy, Duration, Jitter and Shimmer. Test-retest performance was also checked. The voice of DS adults was characterized by a significantly higher Mean Frequency, particularly in males (p<0.0001), by a smaller variation (p=0.0044 in males and p=0.0046 in females) and by a significantly lower level of Energy (p=0.0037 in males and p=0.0025 females). Furthermore, limited to male adults, a shorter Duration (p=0.0156) and a smaller value of Shimmer (p=0.0014) was observed. The difference between DS children and age-matched controls was limited, reaching significance only for the Coefficient of Variation (CV) (p=0.031). The difference in Mean Frequency between adults and children was more evident in the control males than in all other groups. The lack of marked difference between voice characteristics of children with and without DS is outlined by findings. Pearson's correlation coefficients on repeated productions ranged from 0.23 (Jitter) to 0.86 (Mean Frequency) in children, and from 0.07 (Shimmer) to 0.86 (Mean Frequency) in adults. In the control group, all the coefficients ranged between 0.85 and 0.98. As expected, women had a higher Mean Frequency than men, but the CV was around 0.1 for both. By contrast, children had a significantly higher Mean Frequency and a lower CV. In conclusion, spectral analysis of the human voice is recommended in each laboratory of speech and language rehabilitation to exploit the accuracy of voice descriptors.
Research in developmental disabilities, 2010 · doi:10.1016/j.ridd.2010.04.024