We propose an unsupervised adaptation framework, Self-T Aught Recognizer (ST AR), which leverages unlabeled data to enhance the robustness of automatic speech recognition (ASR) systems in diverse target domains, such as noise and accents. ST AR is developed for prevalent speech foundation models based on Transformer-related architecture with auto-regressive decoding (e.g., Whisper, Canary; SeamlessM4T).

Continuous deep learning architectures enable learning of flexible probabilistic modelsforpredictivemodeling asneuralordinary differential equations (ODEs), and for generative modeling as continuous normalizing flows.