The article discusses a new method for training speech recognition models that involves distilling knowledge from pre-trained models to improve their efficiency and accuracy. The approach uses two submodels, an audio base and a head, which work together to transform the audio signal into written text. The method is applied to various datasets, including LRS2 (BBC), LRS3 (TED), VoxCeleb2, AVSpeech, MV-LRS, and YT31k/YT90k, and shows promising results in improving resource efficiency and data efficiency.
Methodology
The method involves training the VSR model in two steps: (1) providing a pre-trained ASR model with a large corpus of text data to extract high-level speech features, and (2) fine-tuning the model using a smaller dataset of labeled speech recordings to learn the mapping between the audio signal and written text. The approach can be adapted to any ASR model regardless of its architecture or sequence alignment loss used during training.
Key Takeaways
The article presents a novel method for training speech recognition models that improves their efficiency and accuracy by distilling knowledge from pre-trained models. The approach involves two submodels, an audio base and a head, which work together to transform the audio signal into written text. The method is applied to various datasets and shows promising results in improving resource efficiency and data efficiency. By using this method, speech recognition systems can be trained more quickly and accurately without requiring as much data or computational resources.
