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LLMs and TTS generate synthetic dialogues to boost ASR training

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel data augmentation pipeline to improve Automatic Speech Recognition (ASR) for low-resource languages and specialized domains. This method synthesizes realistic dialogues using Large Language Models (LLMs) and Text-to-Speech (TTS) technology, creating speaker-aware simulated conversations. Evaluations on a Hungarian benchmark demonstrated that this synthetic data significantly boosts ASR performance, even outperforming models trained on substantially larger amounts of real speech data. AI

IMPACT Enhances ASR model training efficiency and performance, particularly for data-scarce languages and domains.

RANK_REASON Academic paper detailing a new method for data augmentation in ASR. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  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 …

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