Multi Talker Environment

Multi-talker environments pose significant challenges for speech processing, hindering accurate speech recognition and comprehension. Current research focuses on improving speech enhancement and separation techniques, employing methods like large language models (LLMs) coupled with advanced audio feature extraction, convolutional neural networks (CNNs) incorporating brain-activity data (EEG/ECoG), and beamforming algorithms leveraging noise context. These advancements aim to improve automatic speech recognition (ASR) accuracy and enable more robust human-computer interaction in complex acoustic settings, with applications ranging from hearing aids to smart assistants.