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LLMs generate synthetic conversations to boost ASR training

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a novel method to enhance Automatic Speech Recognition (ASR) training for low-resource languages by generating synthetic conversational data. This pipeline uses LLMs to create dialogues, maps speaker attributes to TTS voice profiles, and assembles simulated conversations. Evaluations on the Hungarian BEA-Dialogue benchmark showed that this synthetic data significantly improves ASR performance, even outperforming models trained on much larger real datasets. AI

IMPACT Synthetic data generation via LLMs and TTS offers a scalable solution for improving ASR in low-resource languages.

RANK_REASON The cluster contains an academic paper detailing a new method for training ASR models.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

LLMs generate synthetic conversations to boost ASR training

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The cluster contains an academic paper detailing a new method for training ASR models.
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · M\'at\'e Gedeon, P\'eter Mihajlik ·

    Efficient ASR Training with Conversations that Never Happened

    arXiv:2606.03957v1 Announce Type: cross Abstract: Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that generates scenario-level dialogues with participa…

  2. arXiv cs.AI TIER_1 English(EN) · Péter Mihajlik ·

    Efficient ASR Training with Conversations that Never Happened

    Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that generates scenario-level dialogues with participant metadata, maps speaker attributes to TTS voice …

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Efficient ASR Training with Conversations that Never Happened

    Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that generates scenario-level dialogues with participant metadata, maps speaker attributes to TTS voice …

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