Human Speech

Human speech research aims to understand and model the complexities of spoken language, encompassing its acoustic properties, linguistic structure, and social context. Current research heavily utilizes deep learning models, particularly convolutional and recurrent neural networks, along with transformer architectures like Conformers and Hyenas, to analyze speech signals, improve speech recognition, and detect various attributes such as emotion, speaker identity, and even mental health indicators. This work has significant implications for advancing human-computer interaction, improving accessibility for individuals with speech impairments, and developing more robust and secure voice-based technologies. Furthermore, cross-species comparisons using similar models are shedding light on the evolution and nature of vocal communication.