Speech intelligibility depends on listener experience: Comparing speech-language pathologists, naïve listeners, and automated recognition

This study examined whether intelligibility judgements differ among experienced speech-language pathologists (SLPs), naïve listeners, and an automated speech recognition system when presented with identical speech stimuli. A total of 87 participants (48 SLPs, 38 naïve listeners, and one automated system) completed a closed-set word identification task and provided continuous intelligibility ratings for a narrative passage. Speech stimuli consisted of synthesised productions incorporating developmental phonological error patterns. Accuracy scores increased from the automated system (M = 56.00%) to naïve listeners (M = 56.32%, SD = 8.6) to SLPs (M = 61.08%, SD = 7.9). Group differences were statistically significant, χ2 (2) = 6.17, p = 0.046, with post hoc analyses indicating differences between SLPs and naïve listeners. Intelligibility ratings also differed across groups, χ2 (2) = 10.11, p = 0.006, with higher ratings observed for SLPs compared to naïve listeners. Findings suggest that listener experience influences intelligibility judgements, with experienced SLPs demonstrating greater accuracy and higher intelligibility ratings than naïve listeners. Performance of naïve listeners was comparable to that of the automated system, with no statistically significant differences observed between these groups. These results highlight the role of listener experience in interpreting disordered speech and have implications for both clinical assessment and the use of automated recognition systems.

Authors

Institutions

Publication Details

Journal
Clinical Linguistics & Phonetics
Published
2026-08-03
DOI
https://doi.org/10.1080/02699206.2026.2704604
Primary Topic
Language Development and Disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Speech intelligibility depends on listener experience: Comparing speech-language pathologists, naïve listeners, and automated recognition

Alan F. Smith, Hannah Stewart
Clinical Linguistics & Phonetics
Language Development and Disorders
article

Speech intelligibility depends on listener experience: Comparing speech-language pathologists, naïve listeners, and automated recognition

Alan F. Smith, Hannah Stewart
article en

Abstract

This study examined whether intelligibility judgements differ among experienced speech-language pathologists (SLPs), naïve listeners, and an automated speech recognition system when presented with identical speech stimuli. A total of 87 participants (48 SLPs, 38 naïve listeners, and one automated system) completed a closed-set word identification task and provided continuous intelligibility ratings for a narrative passage. Speech stimuli consisted of synthesised productions incorporating developmental phonological error patterns. Accuracy scores increased from the automated system (M = 56.00%) to naïve listeners (M = 56.32%, SD = 8.6) to SLPs (M = 61.08%, SD = 7.9). Group differences were statistically significant, χ2 (2) = 6.17, p = 0.046, with post hoc analyses indicating differences between SLPs and naïve listeners. Intelligibility ratings also differed across groups, χ2 (2) = 10.11, p = 0.006, with higher ratings observed for SLPs compared to naïve listeners. Findings suggest that listener experience influences intelligibility judgements, with experienced SLPs demonstrating greater accuracy and higher intelligibility ratings than naïve listeners. Performance of naïve listeners was comparable to that of the automated system, with no statistically significant differences observed between these groups. These results highlight the role of listener experience in interpreting disordered speech and have implications for both clinical assessment and the use of automated recognition systems.

Clinical Linguistics & Phonetics
University of Louisville Hospital (US)
Openalex Percentile: Top 4%
Language Development and Disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.