Pre Trained Speech Model

Pre-trained speech models leverage large datasets to learn robust representations of speech, enabling efficient adaptation to various downstream tasks like speech recognition, emotion recognition, and speaker verification. Current research emphasizes improving these models' efficiency and robustness, focusing on techniques like adapter tuning, prompt engineering, and innovative training strategies such as incorporating textual data or brain activations to refine representations. This work is significant because it reduces the reliance on extensive labeled data, improves performance on low-resource languages and challenging acoustic conditions, and facilitates the development of more versatile and accurate speech processing systems across diverse applications.