Self Supervised Speech Model

Self-supervised speech models learn generalizable representations of speech from unlabeled data, aiming to improve downstream task performance and reduce reliance on expensive labeled datasets. Current research focuses on evaluating these models (e.g., using embedding rank), adapting them for specific applications like stuttering detection and speaker verification (often employing architectures like HuBERT and wav2vec 2.0), and optimizing their efficiency through techniques such as early exiting and adapter tuning. These advancements are significant because they enable more robust and resource-efficient speech processing across diverse applications, including speech recognition, emotion recognition, and even physiological signal prediction from speech.