Topic
data-efficient
Artificial Intelligence #zero-shot#tts
ZeSTA Framework Enhances Zero-Shot TTS Augmentation for Data-Efficient Personalized Speech Synthesis
Researchers propose ZeSTA, a domain-conditioned training framework that distinguishes real and synthetic speech via a lightweight domain embedding, combined with real-data oversampling. The approach improves speaker similarity over naive synthetic augmentation while preserving intelligibility and perceptual quality in low-resource personalized speech synthesis.
Jun 20, 2026 1 source
Artificial Intelligence #artificial intelligence#agent framework
Edu-Theater: A Data-Efficient LLM Agent Framework for Scalable Learner Behavior Simulation
Edu-Theater is a new LLM-powered agent framework for simulating learner behavior. It uses a cohort-aware roll-call paradigm to reduce data and computation needs. Experiments on two real-world datasets show higher accuracy with fewer LLM calls, enabling scalable synthetic data generation for adaptive testing.
Jun 16, 2026 1 source