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 …

Recurrent sequence-to-sequence models using encoder-decoder architecture have made
great progress in speech recognition task. However, they suffer from the drawback of slow …

Deep learning (DL) creates impactful advances following a virtuous recipe: model
architecture search, creating large training data sets, and scaling computation. It is widely …

In this paper, we develop a deep learning based semantic communication system for
speech transmission, named DeepSC-ST. We take the speech recognition and speech …

Recently, Transformer based end-to-end models have achieved great success in many
areas including speech recognition. However, compared to LSTM models, the heavy …

The widespread application of deep learning has changed the landscape of computation in
data centers. In particular, personalized recommendation for content ranking is now largely …

We present state-of-the-art automatic speech recognition (ASR) systems employing a
standard hybrid DNN/HMM architecture compared to an attention-based encoder-decoder …

We present a state-of-the-art speech recognition system developed using end-to-end deep
learning. Our architecture is significantly simpler than traditional speech systems, which rely …

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 …