3 accepted papers
In this work, we propose a Multi-Window Masked Autoencoder (MW-MAE) fitted with a novel Multi-Window Multi-Head Attention (MW-MHA) module that facilitates the modelling of local-global interactions in every decoder transformer block through attention heads of several distinct local and global window…
Conventionally, speech emotion recognition has been approached by utterance or turn-level modelling of input signals, either through extracting hand-crafted low-level descriptors, bag-of-audio-words features or by feeding long-duration signals directly to deep neural networks (DNNs). While this appr…
Majority of the recent approaches for text-independent speaker recognition apply attention or similar techniques for aggregation of frame-level feature descriptors generated by a deep neural network (DNN) front-end. In this paper, we propose methods of convolutional attention for independently model…