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 …

This paper investigates the impact of word-based RNN language models (RNN-LMs) on the
performance of end-to-end automatic speech recognition (ASR). In our prior work, we have …

The acoustic-to-word model based on the connectionist temporal classification (CTC)
criterion was shown as a natural end-to-end (E2E) model directly targeting words as output …

Although great progress has been made on end-to-end (E2E) models for monolingual and
multilingual automatic speech recognition (ASR), there is no successful study for E2E …

The Transformer self-attention network has recently shown promising performance as an
alternative to recurrent neural networks (RNNs) in end-to-end (E2E) automatic speech …

Attention-based sequence-to-sequence architectures have made great progress in the
speech recognition task. The SpeechTransformer, a no-recurrence encoder-decoder …

Systems and methods are disclosed for customizing a neural network for a custom dataset,
when the neural network has been trained on data from a general dataset. The neural …

Visual and audiovisual speech recognition are witnessing a renaissance which is largely
due to the advent of deep learning methods. In this paper, we present a deep learning …

Encoder-decoder models for acoustic-to-word (A2W) automatic speech recognition (ASR)
are attractive for their simplicity of architecture and run-time latency while achieving state-of …