arXiv:2606.03957v1 Announce Type: new 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 comprehens
The increasing sophistication of large language models and text-to-speech technologies enables the generation of high-quality synthetic conversational data, critical for ASR training.
This development addresses a fundamental data scarcity problem in AI, particularly for lower-resource languages and niche domains, accelerating the development and deployment of robust conversational AI.
The reliance on expensive and hard-to-acquire real-world conversational data for ASR training is diminishing, opening new avenues for rapid model development and customization.
- · AI developers in niche domains
- · Companies operating in lower-resource language markets
- · Large Language Model providers
- · Speech technology companies
- · Traditional data collection services for ASR
- · Companies reliant on data scarcity as a barrier to entry
More widespread and accurate conversational AI applications become feasible across diverse languages and specialized industries.
The cost of developing and deploying advanced voice interfaces in various sectors significantly decreases, democratizing access to AI capabilities.
New ethical and regulatory challenges related to synthetic voice generation and the potential for deepfake audio may emerge or intensify.
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