3 accepted papers
In this paper, we present the system developed by our team, CCATTS, for the LIMMITS’25 challenge, focusing on few-shot and zero-shot TTS. We adopt a two-stage TTS strategy. In track 1, we fine-tune the pre-trained ZMM-TTS model and successfully achieve multilingual low-latency TTS. In track 2, we pr…
Federated Learning (FL) is a distributed framework that enables multi-participant collaborative model training without the need for data sharing. Despite its advantages, FL is vulnerable to poisoning and inference attacks, which compromise model accuracy and data privacy. Trusted execution environme…
Supervised learning and self-supervised learning address different facets. Supervised learning achieves high accuracy, but it requires numerous expensive labeled data indeed. Correspondingly, self-supervised learning, makes use of abundant unlabeled data to learn, but the performance lags behind tha…