The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

In-context learning, as a new paradigm in NLP, allows the model to rapidly adapt to various
tasks with only a handful of prompts and examples. But in computer vision, the difficulties for …

SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the
research and development of neural speech processing technologies by being simple …

Transformers have been recently adapted for large scale image classification, achieving
high scores shaking up the long supremacy of convolutional neural networks. However the …

Conformer has proven to be effective in many speech processing tasks. It combines the
benefits of extracting local dependencies using convolutions and global dependencies …

We present AIST++, a new multi-modal dataset of 3D dance motion and music, along with
FACT, a Full-Attention Cross-modal Transformer network for generating 3D dance motion …

Recurrent Neural Networks (RNNs) have long been the dominant architecture in sequence-
to-sequence learning. RNNs, however, are inherently sequential models that do not allow …

Recently Transformer and Convolution neural network (CNN) based models have shown
promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …