Speech Recognition Module

Speech recognition modules (SRMs) aim to accurately transcribe spoken language, a crucial task with applications ranging from language learning tools to complex information extraction systems. Current research focuses on improving SRM performance in challenging scenarios, such as recognizing children's speech, separating target speakers from background noise in multi-channel recordings, and handling the inherent errors in automatically transcribed speech for downstream tasks like relation extraction. This involves developing advanced architectures like Conformers and integrating spatial features (e.g., 3D location information) to enhance accuracy and efficiency, often employing end-to-end training methods. Advances in SRMs are vital for numerous applications, improving accessibility and enabling new possibilities in areas like education and natural language processing.