Speech Foundation Model

Speech foundation models are large, pre-trained neural networks designed to learn generalizable representations from vast amounts of unlabeled speech data, enabling efficient adaptation to various downstream tasks. Current research emphasizes improving their performance on challenging scenarios like child speech, noisy environments, and low-resource languages, often employing techniques like parameter-efficient fine-tuning and model ensembles, with architectures such as Whisper, Wav2Vec2, and HuBERT playing prominent roles. This work is significant for its potential to advance applications in healthcare (mental health diagnosis, speech disorder assessment), accessibility (improved speech recognition for diverse populations), and security (deepfake detection).