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 …

Automatic speech recognition, especially large vocabulary continuous speech recognition,
is an important issue in the field of machine learning. For a long time, the hidden Markov …

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 …

Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural
language processing models, we propose a unified-modal SpeechT5 framework that …

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 …

In this work, we present a hybrid CTC/Attention model based on a ResNet-18 and
Convolution-augmented transformer (Conformer), that can be trained in an end-to-end …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

Audio-visual speech recognition has received a lot of attention due to its robustness against
acoustic noise. Recently, the performance of automatic, visual, and audio-visual speech …

The CHiME challenge series aims to advance robust automatic speech recognition (ASR)
technology by promoting research at the interface of speech and language processing …

Speaker diarization has been mainly developed based on the clustering of speaker
embeddings. However, the clustering-based approach has two major problems; ie,(i) it is not …