Such sequence-to-sequence models are fully neural, without finite state transducers, a lexicon, or text normalization modules. Training such models is simpler than conventional ASR systems: they do not require bootstrapping from decision trees or time alignments generated from a separate system.
Dec 5, 2017 · In this work, we explore a variety of structural and optimization improvements to our LAS model which significantly improve performance.
In this work, we explore a variety of structural and optimization improvements to our LAS model which significantly improve performance.
End-to-end (E2E) models excel in automatic speech recognition (ASR) by directly mapping speech signals to word sequences [1, 2,3,4,5]. Despite training on large ...
A variety of structural and optimization improvements to the Listen, Attend, and Spell model are explored, which significantly improve performance and a ...
People also ask
What are sequence to sequence models in NLP?
What are speech recognition models?
How to improve an ASR model?
What is ASR and how does it work?
... Sequence-to-sequence models are machine learning models that map an input sequence to an output sequence. They are utilized in numerous NLP tasks, for ...
In this work, we conduct a detailed evaluation of various all-neural, end-to-end trained, sequence-to-sequence models applied to the task of speech ...
Sequence-To-Sequence Speech Recognition - Papers With Code
paperswithcode.com › task › sequence-to...
We present a recurrent encoder-decoder deep neural network architecture that directly translates speech in one language into text in another.
Nov 28, 2018 · Bibliographic details on State-of-the-art Speech Recognition With Sequence-to-Sequence Models.
Recent research has shown that attention-based sequence-to-sequence models such as Listen, Attend, and Spell (LAS) yield comparable results ...