Contextual Automatic Speech Recognition

Contextual automatic speech recognition (ASR) aims to improve speech-to-text accuracy by incorporating contextual information, such as user vocabulary or surrounding text, to better recognize rare words and named entities. Current research focuses on enhancing neural network architectures, including transformer transducers and incorporating techniques like contextual biasing, retrieval augmentation, and multi-modal approaches (e.g., using visual information from slides). These advancements are significant because they address limitations of traditional ASR systems, leading to more robust and accurate transcriptions in diverse and challenging real-world scenarios, such as spoken dialog systems and meeting transcription.