Year: 2025
Type: conference-paper
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.
Authors: Maria Kristina C. Rodriguez, Gheciel Mayce M. Santos, Jennifer C. Dela Cruz, Jmi C. Dela Cruz
Institution: Mapúa University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410236706