Speech Delay Assistive Device for Speech-to-Text Transcription Based on Machine Learning

Despite advances by major companies, existing technologies often misinterpret speech from individuals with speech delays. To address this challenge, a portable machine learning (ML) speech-to-text assistive device was developed for speech-delayed children. The device is composed of a Raspberry Pi 4 and Google Web Speech API and enables the accurate transcription of challenging speech sounds of children aged 6 to 14 years old. The device performs noise reduction and digital transcription. Its performance was validated by speech language pathologists (SLPs). The device achieved 94% word accuracy, 92% sentence accuracy, and a word error rate (WER) of 0 to 14%. The ML-based device is a significant improvement on existing speech therapy tools, offering an accessible solution for speech-delayed children.

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Publication Details

Published
2025-05-08
DOI
https://doi.org/10.3390/engproc2025092060
Citations
1
Primary Topic
Educational Technology and Pedagogy
Type
article
Field-Weighted Citation Impact
2.48
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article

Speech Delay Assistive Device for Speech-to-Text Transcription Based on Machine Learning

Maria Kristina C. Rodriguez, Gheciel Mayce M. Santos, Jennifer C. Dela Cruz
1 citations
Educational Technology and Pedagogy
2.48
article

Speech Delay Assistive Device for Speech-to-Text Transcription Based on Machine Learning

Maria Kristina C. Rodriguez, Gheciel Mayce M. Santos, Jennifer C. Dela Cruz
article en
1 citations

Abstract

Despite advances by major companies, existing technologies often misinterpret speech from individuals with speech delays. To address this challenge, a portable machine learning (ML) speech-to-text assistive device was developed for speech-delayed children. The device is composed of a Raspberry Pi 4 and Google Web Speech API and enables the accurate transcription of challenging speech sounds of children aged 6 to 14 years old. The device performs noise reduction and digital transcription. Its performance was validated by speech language pathologists (SLPs). The device achieved 94% word accuracy, 92% sentence accuracy, and a word error rate (WER) of 0 to 14%. The ML-based device is a significant improvement on existing speech therapy tools, offering an accessible solution for speech-delayed children.

Mapúa University (PH)
Openalex Percentile: Top 11%
Educational Technology and Pedagogy
2.48
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Speech Delay Assistive Device for Speech-to-Text Transcription Based on Machine Learning — Maria Kristina C. Rodriguez, Gheciel Mayce M. Santos, et al. · (2025) | TGRS Research Map | TGRS