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 …

Temporal modeling of objects is a key challenge in multiple-object tracking (MOT). Existing
methods track by associating detections through motion-based and appearance-based …

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 …

Deep neural networks (DNN) techniques have become pervasive in domains such as
natural language processing and computer vision. They have achieved great success in …

Continuous speech separation was recently proposed to deal with the overlapped speech in
natural conversations. While it was shown to significantly improve the speech recognition …

The joint training framework for speech enhancement and recognition methods have
obtained quite good performances for robust end-to-end automatic speech recognition …

Multi-source localization is an important and challenging technique for multi-talker
conversation analysis. This paper proposes a novel supervised learning method using deep …

We present ESPnet-SE, which is designed for the quick development of speech
enhancement and speech separation systems in a single framework, along with the optional …

Today, the Transformer model, which allows parallelization and also has its own internal
attention, has been widely used in the field of speech recognition. The great advantage of …

Lyrics are the words that make up a song, while chords are harmonic sets of multiple notes
in music. Lyrics and chords are generally essential information in music, ie unaccompanied …