Speech Intelligibility

Speech intelligibility research focuses on understanding and improving how well spoken words are perceived, particularly in challenging acoustic conditions or for individuals with hearing impairments. Current research emphasizes developing computational models, often employing deep learning architectures like convolutional neural networks, recurrent neural networks (LSTMs), and generative adversarial networks (GANs), to enhance speech quality and predict intelligibility using various acoustic features and representations (e.g., spectrograms, MFCCs, self-supervised embeddings). These advancements have significant implications for improving assistive listening devices, language learning technologies, and human-computer interaction systems by enhancing the clarity and understandability of speech in diverse contexts.