Unsupervised Speech Representation

Unsupervised speech representation learning aims to automatically extract meaningful features from speech audio without relying on labeled data, enabling applications like speech recognition and synthesis that require less human annotation. Current research focuses on developing robust and context-invariant representations using self-supervised learning methods, often employing deep neural networks such as transformers and vector quantization techniques to handle variable-length speech signals. These advancements are improving the performance of various speech processing tasks and facilitating the development of more efficient and adaptable systems, particularly for resource-constrained environments.