awasthiabhijeet/Error-Driven-ASR-Personalization
Code for "Error-driven Fixed-Budget ASR Personalization for Accented Speakers" in ICASSP 2021
This project helps speech scientists and researchers improve Automatic Speech Recognition (ASR) systems for speakers with diverse accents. It takes existing accented speech audio and its human-verified transcripts to identify common recognition errors. The output is a highly targeted, smaller subset of data that can be used to fine-tune an ASR model, leading to better accuracy for accented speech without needing a massive amount of new data.
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Use this if you need to personalize an ASR system to better understand specific accented speakers using a limited budget of additional training data.
Not ideal if you are looking for an off-the-shelf ASR system or if you have an abundance of new, fully transcribed accented speech data for fine-tuning.
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Python
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Last pushed
Jun 13, 2021
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