Intelligibility Prediction

Intelligibility prediction focuses on automatically assessing how well a speech signal can be understood, often in challenging listening conditions or for individuals with hearing impairments. Current research emphasizes developing non-intrusive methods, leveraging deep learning architectures like Long Short-Term Memory (LSTM) networks and pre-trained speech foundation models (SFMs), often incorporating acoustic and modulation spectrograms or even raw waveforms as input features. These advancements aim to improve objective speech quality and intelligibility assessment, with significant implications for hearing aid design, speech enhancement algorithms, and the broader field of speech perception research.