Latent Speech

Latent speech representation focuses on capturing the underlying, meaningful information in speech signals, aiming to improve various speech processing tasks. Current research heavily utilizes deep learning models, including variational autoencoders (VAEs), diffusion models, and convolutional neural networks (CNNs), to learn these representations, often incorporating techniques like self-supervised learning and optimal transport for improved performance. This work has significant implications for applications such as speech synthesis, recognition, and intelligibility assessment, offering potential for more natural and robust systems across diverse scenarios, including those involving noisy or pathological speech. The ability to effectively manipulate and utilize these latent representations is driving advancements in several areas of speech technology.