Unpaired Speech

Unpaired speech data, consisting of speech and text recordings without corresponding pairings, is increasingly used to train speech processing models, addressing the limitations of data scarcity and annotation costs. Current research focuses on leveraging unpaired data through techniques like generative adversarial networks (GANs), diffusion models, and self-supervised pre-training methods, often incorporating transformer architectures to improve speech recognition, synthesis, and voice conversion. These advancements are particularly impactful for low-resource languages and applications where paired data is difficult or expensive to obtain, leading to more robust and widely accessible speech technologies.