3 accepted papers
In recent years, there has been a surge in the use of a pre-trained speech model as a feature extractor for speaker verification (SV). To reduce model complexity, researchers transfer knowledge from a pre-trained model to a lightweight student model, enabling the latter to reach a performance level…
Recent works suggest that decoupling the information of non-target speakers from that of the target speaker in knowledge distillation (KD) and subsequently emphasizing the former can lead to significant performance improvement. However, a well-trained teacher model typically produces almost zero non…
In this paper, we propose TrInk, a Transformer-based model for ink generation, which effectively captures global dependencies. To better facilitate the alignment between the input text and generated stroke points, we introduce scaled positional embeddings and a Gaussian memory mask in the cross-atte