Jul 2026· Journal of Speech, Language and Hearing Research· Vol 69, pp.
1-12
· 0 citations· 25 references
Medicine
TL;DR
Speech compression, particularly in low-bandwidth condition, impacts acoustic fidelity and intelligibility, though expert listeners maintain reliable perceptual judgments, suggesting SLPs could adjust for compression artifacts when making perceptual judgments.
Abstract
Purpose
This study aimed to evaluate how speech signal compression algorithms affect the acoustic and perceptual characteristics of dysarthric speech. As telepractice becomes more common in speech-language pathology, particularly in the assessment of dysarthria, understanding the impact of telecommunication compression on speech fidelity is essential for clinical decision making.
Method
Speech samples were collected from 15 individuals with dysarthria, recorded locally and simultaneously via Zoom (Version 6.2.11). The Opus codec was used to process the locally recorded speech samples under fullband, wideband, and narrowband compression conditions. Acoustic measures were derived for all samples. Twenty experienced speech-language pathologists (SLPs) rated vocal quality and nasality for sustained vowels and rated articulatory precision and orthographically transcribed sentences across each condition.
Results
Narrowband compression was associated with significantly degraded voice quality measures (e.g., harmonic-to-noise ratio, shimmer) and reduced transcription accuracy. Despite these changes in acoustics and intelligibility, ratings of articulatory precision, nasality, and vocal quality remained stable across conditions, suggesting SLPs could adjust for compression artifacts when making perceptual judgments.
Conclusions
Speech compression, particularly in low-bandwidth (i.e., narrow) condition, impacts acoustic fidelity and intelligibility, though expert listeners maintain reliable perceptual judgments. These findings underscore the need to consider compression effects in telepractice and highlight the importance of developing optimized protocols for remote dysarthria assessment.
Speech-language pathologists use voice quality metrics of raw speech to assess dysarthria. Instead of using raw speech, a deep learning based diagnostic method that extracts these metrics from time-frequency speech representations will be reliable and preserves speaker identity. In this work a regression-based deep...
Aurobindo S, R. M, Prakash Ramachandran· Scientific Reports· 0 citations
This study investigated whether different types of dysarthric speech (with comparable baseline intelligibility) are equally susceptible to background noise. Using intrinsically degraded speech from four individuals with dysarthria, each representing a distinct motor speech disorder subtype, we examined how intelligibil...
Katerina A. Tetzloff, Kian Fallah, Eric W. Healy et al.· Journal of the Acoustical So...· 0 citations
PURPOSE
This study examined the impact of clear speech on perceived naturalness. We compared habitual speech (HS), untrained clear speech (UCS), and clear speech after a brief training (trained clear speech [TCS]) and examined whether acoustic variables differed between conditions and predicted naturalness.
METHOD
Tw...
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Renuka Chandrakanth· Natural Resources for Human...· 0 citations
PURPOSE
The minimally detectable change (MDC) provides a threshold for interpreting changes in outcome measures that are outside of measurement error. There is emerging evidence that dysarthria etiology and speech severity impact the MDC of speech intelligibility; however, an investigation of these factors is needed to...
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