A fine-tuned Whisper tiny.en model reaches 15.9% WER on children's speech and runs in real time on a Raspberry Pi, with low-rank compression trading an 11% relative WER rise for lighter computation.
This tech- nology is at the core of various ‘AI-based products’ for chil- dren, at home or in the classroom
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Adapting Whisper for Lightweight and Efficient Automatic Speech Recognition of Children for On-device Edge Applications
A fine-tuned Whisper tiny.en model reaches 15.9% WER on children's speech and runs in real time on a Raspberry Pi, with low-rank compression trading an 11% relative WER rise for lighter computation.