The key to fixing speech-to-text errors isn't in overhauling the recognition engine but in implementing a lightweight post-transcription text processing layer.
A compact, PyTorch-based code can adapt to user corrections, thus enhancing the speech-to-text experience by personalizing transcriptions of homophones.
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