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 …

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 …

Sequence-to-sequence models have been widely used in end-to-end speech processing,
for example, automatic speech recognition (ASR), speech translation (ST), and text-to …

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 …

Abstract Deep Neural Networks (DNN) are nothing but neural networks with many hidden
layers. DNNs are becoming popular in automatic speech recognition tasks which combines …

In the emerging world of metaverses, it is essential for speech communication systems to be
aware of context to interact with virtual assets in the 3D world. This paper proposes the …

Conventional automatic speech recognition (ASR) based on a hidden Markov model
(HMM)/deep neural network (DNN) is a very complicated system consisting of various …

The state-of-the-art neural network architecture named Transformer has been used
successfully for many sequence-tosequence transformation tasks. The advantage of this …

We present competitive results using a Transformer encoder-decoder-attention model for
end-to-end speech recognition needing less training time compared to a similarly …