Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS),
subsume the acoustic, pronunciation and language model components of a traditional …

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 …

Attention-based end-to-end models such as Listen, Attend and Spell (LAS), simplify the
whole pipeline of traditional automatic speech recognition (ASR) systems and become …

Sequence-to-sequence models provide a simple and elegant solution for building speech
recognition systems by folding separate components of a typical system, namely acoustic …

Attention-based recurrent neural encoder-decoder models present an elegant solution to the
automatic speech recognition problem. This approach folds the acoustic model …

Recently, there has been a growing interest in end-to-end speech recognition that directly
transcribes speech to text without any predefined alignments. In this paper, we explore the …

Direct acoustics-to-word (A2W) models in the end-to-end paradigm have received
increasing attention compared to conventional subword based automatic speech …

In this paper we present an end-to-end speech recognition model with Transformer
encoders that can be used in a streaming speech recognition system. Transformer …

Recently, a few novel streaming attention-based sequence-to-sequence (S2S) models have
been proposed to perform online speech recognition with linear-time decoding complexity …

Attention-based sequence-to-sequence models for speech recognition jointly train an
acoustic model, language model (LM), and alignment mechanism using a single neural …