DanielLin94144/Test-time-adaptation-ASR-SUTA
Test-time adaptation for speech recognition model by single utterance. The official implementation of "Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition" paper.
This project improves the accuracy of speech recognition models when they encounter unfamiliar accents, background noise, or speaking styles. It takes an audio recording and an existing speech-to-text model, then adapts the model in real-time to transcribe that specific recording more accurately. This is designed for researchers and engineers working with automatic speech recognition (ASR) systems.
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Use this if you need to improve the word error rate of an existing ASR model on individual, diverse audio inputs without retraining the entire model or needing the original training data.
Not ideal if you are looking for a pre-built, production-ready speech-to-text service or if your ASR model is not based on the CTC architecture.
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Python
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Apr 01, 2022
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