DEFINE: Exemplar-Guided Accent Control for Zero-Shot TTS

Ambuj Mehrish, Abhinaba Roy, Alex Ivanov et al.1 min readHugging FaceSep 30, 2026

Key Takeaway for Students

Zero-shot text-to-speech (TTS) can reproduce an unseen speaker from a short reference recording, but typically entangles speaker identity and accent within the same reference. We introduce DEFINE, an end-to-end framework that decouples these factors by conditioning speaker identity and target accent on separate audio exemplars. A single inference-time guidance weight continuous

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This research introduces a novel approach to zero-shot TTS by decoupling speaker identity and accent, allowing for independent control. The use of parameter-efficient LoRA adaptation and inference-time guidance for accent strength provides significant flexibility and efficiency.

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Career Insight

medium demand

Why it matters

Skills to learn

Understanding of zero-shot TTS architecturesImplementing LoRA for parameter-efficient adaptationDesigning exemplar-based conditioning mechanismsDeveloping and evaluating accent control in TTS modelsWorking with F5-TTS or similar end-to-end TTS frameworks

Interview questions

Explain how DEFINE decouples speaker identity and accent in zero-shot TTS. What are the architectural components that enable this separation?
Describe the role of LoRA adaptation in DEFINE. How does it contribute to parameter efficiency and what are its implications for model deployment?
Design a system for deploying DEFINE in a production environment where users can dynamically adjust accent strength. Consider latency, resource utilization, and scalability.

Relevant for ~18 months · generated by the enrichment pipeline