A current problem in NLP is massaging and processing low-resource languages which lack
useful training attributes such as supervised data, number of native speakers or experts, etc …

Current research on spoken language translation (SLT) has to confront with the scarcity of
sizeable and publicly available training corpora. This problem hinders the adoption of neural …

End-to-end spoken language translation (SLT) has recently gained popularity thanks to the
advancement of sequence to sequence learning in its two parent tasks: automatic speech …

Speech translation (ST) is an increasingly popular topic of research, partly due to the
development of benchmark datasets. Nevertheless, current datasets cover a limited number …

We present ESPnet-ST, which is designed for the quick development of speech-to-speech
translation systems in a single framework. ESPnet-ST is a new project inside end-to-end …

We explore multitask models for neural translation of speech, augmenting them in order to
reflect two intuitive notions. First, we introduce a model where the second task decoder …

Abstract Speech-to-Text (ST) translation pertains to the task of converting speech signals in
one language to text in another language. It finds its application in various domains, such as …

We present a simple approach to improve direct speech-to-text translation (ST) when the
source language is low-resource: we pre-train the model on a high-resource automatic …

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 …

In this paper, we propose a simple yet effective framework for multilingual end-to-end
speech translation (ST), in which speech utterances in source languages are directly …