Researchers have developed a new method for correcting disfluencies in multilingual speech transcripts using large language models (LLMs). The pipeline first identifies disfluent tokens and then uses these signals to fine-tune an LLM for rewriting transcripts into fluent text. A contrastive learning objective was added to penalize the reproduction of disfluent tokens, ensuring grammar and meaning are preserved. Experiments in Hindi, Bengali, and Marathi demonstrated significant improvements over existing baselines, offering a practical solution for speech-driven NLP systems. AI
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IMPACT Enhances the accuracy and usability of speech-driven NLP applications by improving transcript quality.
RANK_REASON The cluster contains an academic paper detailing a new methodology for speech correction using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]