Automatic Speech Recognition Model

Automatic speech recognition (ASR) models aim to accurately convert spoken language into text, a crucial task with broad applications. Current research emphasizes improving ASR performance in challenging scenarios, such as low-resource languages, accented speech, and noisy environments, often leveraging large language models (LLMs) and techniques like parameter-efficient fine-tuning and self-supervised learning. These advancements are driven by the need for more robust, accurate, and equitable ASR systems across diverse languages and speaker demographics, impacting fields ranging from healthcare to legal proceedings.