Contextual Asr

Contextual ASR aims to improve automatic speech recognition (ASR) accuracy by incorporating contextual information, such as lists of relevant words or phrases, to better handle rare or out-of-vocabulary terms. Current research focuses on integrating contextual knowledge into various ASR architectures, including end-to-end models and hybrid CTC/attention systems, often employing techniques like attention mechanisms and pointer generators to effectively bias the model's output. These advancements are significant because they address a major limitation of traditional ASR systems, leading to improved performance in diverse applications, particularly those involving specialized vocabularies or conversational contexts.