Automatic Speech Recognition Error Correction

Automatic speech recognition (ASR) error correction aims to improve the accuracy and readability of ASR transcripts by leveraging the power of large language models (LLMs). Current research focuses on refining LLMs for this task through techniques like prompt engineering, constrained decoding using N-best lists or lattices, and multi-modal approaches incorporating visual cues (e.g., lip movements) or phonetic information. These advancements are significant because accurate transcriptions are crucial for various applications, including emotion recognition, clinical documentation, and improving the overall performance of downstream tasks that rely on speech-to-text conversion.