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 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 …

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

Encoder-decoder based sequence-to-sequence models have demonstrated state-of-the-art
results in end-to-end automatic speech recognition (ASR). Recently, the transformer …

In the last few years, an emerging trend in automatic speech recognition research is the
study of end-to-end (E2E) systems. Connectionist Temporal Classification (CTC), Attention …

Modern neural architectures for classification tasks are trained using the cross-entropy loss,
which is widely believed to be empirically superior to the square loss. In this work we …

End-to-end (E2E) models have shown to outperform state-of-the-art conventional models for
streaming speech recognition [1] across many dimensions, including quality (as measured …

Attention is a very popular and effective mechanism in artificial neural network-based
sequence-to-sequence models. In this survey paper, a comprehensive review of the different …

Recently, there has been a strong push to transition from hybrid models to end-to-end (E2E)
models for automatic speech recognition. Currently, there are three promising E2E methods …

House price prediction is a popular topic, and research teams are increasingly performing
related studies by using deep learning or machine learning models. However, because …