Towards Interfacing Large Language Models With ASR Systems Using Confidence Measures And Prompting

Abstract

As large language models (LLMs) grow in parameter size and capabilities, such as interaction through prompting, they open up new ways of interfacing with automatic speech recognition (ASR) systems beyond rescoring n-best lists. This work investigates post-hoc correction of ASR transcripts with LLMs. To avoid introducing errors into likely accurate transcripts, we propose a range of confidence-based filtering methods. Our results indicate that this can improve the performance of less competitive ASR systems.

Stats

• citations8
• S2 citations—
• github stars0
• HF likes0
• heat score7.16
• arxiv keynaderi2024towards

Related papers

• Multi-stage Large Language Model Correction For Speech Recognition (2023)0.00
• Exploring The Integration Of Large Language Models Into Automatic Speech Recognition Systems: An Empirical Study (2023)8.09
• Context And System Fusion In Post-asr Emotion Recognition With Large Language Models (2024)0.00
• Effective Text Adaptation For Llm-based ASR Through Soft Prompt Fine-tuning (2024)5.84
• Revise, Reason, And Recognize: Llm-based Emotion Recognition Via Emotion-specific Prompts And ASR Error Correction (2024)7.81
• ASR Error Correction Using Large Language Models (2024)9.41
• Chain Of Correction For Full-text Speech Recognition With Large Language Models (2025)0.00
• A Comprehensive Solution To Connect Speech Encoder And Large Language Model For ASR (2024)0.00