Speech Analysis
Speech analysis is a rapidly evolving field focused on understanding and manipulating spoken language using computational methods, aiming to improve human-computer interaction and address challenges in healthcare and other domains. Current research emphasizes developing robust models, often based on transformer networks and neural codecs, for tasks such as speech recognition, emotion detection, and generation, including handling multi-speaker scenarios and low-resource languages. These advancements have significant implications for applications ranging from improved accessibility for individuals with speech impairments to more natural and intuitive interfaces for various technologies, as well as enabling new diagnostic tools in healthcare.
Papers
Phonemic Representation and Transcription for Speech to Text Applications for Under-resourced Indigenous African Languages: The Case of Kiswahili
Ebbie Awino, Lilian Wanzare, Lawrence Muchemi, Barack Wanjawa, Edward Ombui, Florence Indede, Owen McOnyango, Benard Okal
Learning to Compute the Articulatory Representations of Speech with the MIRRORNET
Yashish M. Siriwardena, Carol Espy-Wilson, Shihab Shamma