Speech Mapping

Speech mapping focuses on developing computational models that accurately translate between speech and other modalities, such as text or lip movements. Current research emphasizes improving the accuracy and efficiency of these mappings using various deep learning architectures, including transformers, variational autoencoders, and Siamese networks, often incorporating techniques like chain-of-thought prompting and pseudo-labeling to address data limitations. These advancements are driving progress in applications like voice-controlled devices, speech-to-speech translation, and improved automatic speech recognition, particularly in challenging acoustic environments. The ultimate goal is to create more natural and robust human-computer interaction systems.