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Found 4 papers, 0 papers with code
Audio-AdapterFusion: A Task-ID-free Approach for Efficient and Non-Destructive Multi-task Speech Recognition
Adapters are an efficient, composable alternative to full fine-tuning of pre-trained models and help scale the deployment of large ASR models to many tasks.
A Comparison of Semi-Supervised Learning Techniques for Streaming ASR at Scale
Unpaired text and audio injection have emerged as dominant methods for improving ASR performance in the absence of a large labeled corpus.
Dual Learning for Large Vocabulary On-Device ASR
Dual learning is a paradigm for semi-supervised machine learning that seeks to leverage unsupervised data by solving two opposite tasks at once.
Improving Rare Word Recognition with LM-aware MWER Training
Language models (LMs) significantly improve the recognition accuracy of end-to-end (E2E) models on words rarely seen during training, when used in either the shallow fusion or the rescoring setups.
