Fairseq S2T: Fast speech-to-text modeling with fairseq
We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such
as end-to-end speech recognition and speech-to-text translation. It follows fairseq's careful …
as end-to-end speech recognition and speech-to-text translation. It follows fairseq's careful …
STEMM: Self-learning with speech-text manifold mixup for speech translation
How to learn a better speech representation for end-to-end speech-to-text translation (ST)
with limited labeled data? Existing techniques often attempt to transfer powerful machine …
with limited labeled data? Existing techniques often attempt to transfer powerful machine …
Learning shared semantic space for speech-to-text translation
Having numerous potential applications and great impact, end-to-end speech translation
(ST) has long been treated as an independent task, failing to fully draw strength from the …
(ST) has long been treated as an independent task, failing to fully draw strength from the …
Speech translation and the end-to-end promise: Taking stock of where we are
M Sperber, M Paulik - arXiv preprint arXiv:2004.06358, 2020 - arxiv.org
Over its three decade history, speech translation has experienced several shifts in its
primary research themes; moving from loosely coupled cascades of speech recognition and …
primary research themes; moving from loosely coupled cascades of speech recognition and …
Voice transformer network: Sequence-to-sequence voice conversion using transformer with text-to-speech pretraining
We introduce a novel sequence-to-sequence (seq2seq) voice conversion (VC) model based
on the Transformer architecture with text-to-speech (TTS) pretraining. Seq2seq VC models …
on the Transformer architecture with text-to-speech (TTS) pretraining. Seq2seq VC models …
SimulMT to SimulST: Adapting simultaneous text translation to end-to-end simultaneous speech translation
Simultaneous text translation and end-to-end speech translation have recently made great
progress but little work has combined these tasks together. We investigate how to adapt …
progress but little work has combined these tasks together. We investigate how to adapt …
A study of transformer-based end-to-end speech recognition system for Kazakh language
Today, the Transformer model, which allows parallelization and also has its own internal
attention, has been widely used in the field of speech recognition. The great advantage of …
attention, has been widely used in the field of speech recognition. The great advantage of …
CMOT: Cross-modal mixup via optimal transport for speech translation
End-to-end speech translation (ST) is the task of translating speech signals in the source
language into text in the target language. As a cross-modal task, end-to-end ST is difficult to …
language into text in the target language. As a cross-modal task, end-to-end ST is difficult to …
Listen, understand and translate: Triple supervision decouples end-to-end speech-to-text translation
An end-to-end speech-to-text translation (ST) takes audio in a source language and outputs
the text in a target language. Existing methods are limited by the amount of parallel corpus …
the text in a target language. Existing methods are limited by the amount of parallel corpus …
A comparative study on end-to-end speech to text translation
Recent advances in deep learning show that end-to-end speech to text translation model is
a promising approach to direct the speech translation field. In this work, we provide an …
a promising approach to direct the speech translation field. In this work, we provide an …