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LLMs improve multilingual speech correction by tuning for fluency

By PulseAugur Editorial · [1 sources] ·

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

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]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs improve multilingual speech correction by tuning for fluency

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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]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Asif Ekbal ·

    Mind the Pause: Disfluency-Aware Objective Tuning for Multilingual Speech Correction with LLMs

    Automatic Speech Recognition (ASR) transcripts often contain disfluencies, such as fillers, repetitions, and false starts, which reduce readability and hinder downstream applications like chatbots and voice assistants. If left unaddressed, such disfluencies can significantly degr…

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