Demographic and Acoustic Factors Related to Automatic Speech Recognition Inaccuracies for Child African American English Speakers.
Purpose: This study investigated the relationship between acoustic measures and Google's Speech-to-Text inaccuracies in recognizing speech of children ages 4-9 years who speak African American English (AAE). Method: Audio recordings were collected from 11 AAE-speaking children with speech stimuli ta...
| Published in: | Perspectives of the ASHA Special Interest Groups Vol. 10; no. 6; pp. 2278 - 2298 |
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| Main Authors: | , , , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
American Speech-Language-Hearing Association
Dec2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190171850&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190171850 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2381473X KTSD jtl: Perspectives of the ASHA Special Interest Groups issn: 2381473X maglogo: N pubinfo: dt: Dec2025 vid: 10 iid: 6 pid: 42 pub: American Speech-Language-Hearing Association place: Rockville, Maryland artinfo: ui: 190171850 190171850 190171850 10.1044/2025_PERSP-25-00052 190171850 ppf: 2278 ppct: 20 formats: fmt: @attributes: type: P tig: atl: Demographic and Acoustic Factors Related to Automatic Speech Recognition Inaccuracies for Child African American English Speakers. aug: au: Fletcher, Brittany N. Hsu, Wei-Wen Novak, Vesna D. Wilkens, Mary E. Hobek, Amy W. Pratt, Amy S. Leon, Michelle Harrell, Kimmerly McKenna, Victoria S. affil: Department of Communication Sciences and Disorders, University of Cincinnati, OH sug: subj: Voice Recognition Systems Methods Sociodemographic Factors Acoustics Evaluation African American English Human Funding Source Male Female Child, Preschool Child Exploratory Research Pilot Studies Descriptive Statistics Logistic Regression ROC Curve Confidence Intervals Speech Production Measurement Accents and Dialects Vowels Voice Evaluation Cues Speech Evaluation Child, Preschool: 2-5 years Child: 6-12 years Male Female ab: Purpose: This study investigated the relationship between acoustic measures and Google's Speech-to-Text inaccuracies in recognizing speech of children ages 4-9 years who speak African American English (AAE). Method: Audio recordings were collected from 11 AAE-speaking children with speech stimuli targeting final plosive variations observed within the AAE dialect. Dialectal density was measured using the Diagnostic Evaluation of Language Variation Language Screener. Recordings were transcribed using Google's Speech-to-Text application (Google Voice), and inaccuracies were determined through comparison to researcher-extracted transcriptions. Acoustic measures from vowels preceding final plosives (including vowel duration, fundamental frequency, average first formant) were extracted using Praat and a custom MATLAB algorithm. Individual mixed-effects logistic regression models were conducted to analyze the relationships between acoustic measures and transcription accuracy (accurate vs. inaccurate) for voiced and voiceless plosives separately. Results: There were no significant differences between inaccuracy rates for voiced and voiceless plosive productions, nor were acoustic measures predictive of automatic speech recognition inaccuracy. However, age and dialect density were significantly related to voiceless plosive accuracy. Conclusions: The complexities of voice, motor, and articulatory development within children can be characterized by acoustic measures. These measures inform acoustic algorithms created for speech technology. Research on acoustic measures in young child AAE speech, with considerations for dialect variability and age, will enhance speech recognition technology and clinical best practices. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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