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ARXIV:2606.03957 · SPEECH AI · SUBMITTED 03 JUN · 20:40 UTC · FRESHNESS FRESH
ARXIV:2606.03957SPEECH AISUBMITTED 03 JUN · 20:40 UTCFRESHNESS FRESHMáté Gedeon · Péter Mihajlik · arXiv
An AI-powered pipeline that generates realistic synthetic conversations to dramatically improve ASR performance for low-resource languages and niche domains, outperforming models trained on significantly more real data.
Opportunity summary
Pain An AI-powered pipeline that generates realistic synthetic conversations to dramatically improve ASR performance for low-resource languages and niche domains, outperforming models trained on significantly more real data.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
An AI-powered pipeline that generates realistic synthetic conversations to dramatically improve ASR performance for low-resource languages and niche domains, outperforming models trained on significantly more real data. We propose an augmentation pipeline that generates…
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…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. The results show that synthetic conversations consistently improve speech recognition performance, but generator choice and data composition strongly affect the gains. Code availability is…
Speech AI moved forward this cycle; last verified June 2026. Public score 8.0/10. Production flags indicate code availability.
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An AI-powered pipeline that generates realistic synthetic conversations to dramatically improve ASR performance for low-resource languages and niche domains, outperforming models trained on significantly more real data.
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10.48550/arXiv.2606.03957An AI-powered pipeline that generates realistic synthetic conversations to dramatically improve ASR performance for low-resource languages and niche domains, outperforming models trained on significantly more real data.
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 participant metadata, maps speaker attributes to TTS voice profiles, and assembles synthesized utterances into speaker-aware simulated conversations. We evaluated five LLM families under single-generator, fixed-budget mixture, and scale-up settings using the same FastConformer-Large training recipe for each one. We ran comprehensive evaluations on the Hungarian BEA-Dialogue benchmark corpus, with the method itself being applicable to any language given the resources for each component. The results show that synthetic conversations consistently improve speech recognition performance, but generator choice and data composition strongly affect the gains. Our largest training configuration, using only 67 hours of real conversations and 636 hours of simulated data, achieves better performance on the evaluation benchmark than a zero-shot model trained on 2700 hours of Hungarian speech. These findings indicate that LLM-generated conversational data synthesized with TTS is a practical complement to real conversational corpora for speech model training.
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unverified0 refs; 3 sources; 50% coverage.
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PROBLEM
An AI-powered pipeline that generates realistic synthetic conversations to dramatically improve ASR performance for low-resource languages and niche domains, outperforming models trained on significantly more real data. We propose an augmentation pipeline that generates scenario...
METHOD
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...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. The results show that synthetic conversations consistently improve speech recognition performance, but generator choice and data composition strongly affect the gains. Code availability is flagged in the...
WHY NOW
Speech AI moved forward this cycle; last verified June 2026. Public score 8.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 15, "author": "M\u00e1t\u00e9 Gedeon; P\u00e9ter Mihajlik", "title": "Efficient ASR Training with Conversations that Never Happened", "creation date": null
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An AI-powered pipeline that generates realistic synthetic conversations to dramatically improve ASR performance for low-resource languages and niche domains, outperforming models trained on significantly more real data.
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Speech AI
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