End-to-End Neural Approaches for Speech Translation – ON-TRAC
The ON-TRAC project proposes to radically change the architectures used currently in speech translation. It is based on end-to-end neural models for machine translation and focuses on light and portable speech translation applications that Airbus is developing for security operations in theaters of operation.
Beyond the study of end-to-end approaches based on language pairs associated with large-scale learning data, ON-TRAC will study the development of models for poorly endowed oral or dialect languages.
An end-to-end approach to speech translation as we envision it would allow us to review the methodology of data collection for the development of a speech translation system.
Indeed, with this approach, a transcription of the source language becomes unnecessary: the cost of producing the data needed to learn a speech translation system is therefore greatly reduced and the development of such a system for new languages (including those without a writing system) would be facilitated and accelerated.
Since the project targets portable translation applications, ON-TRAC is also interested in studying the computational time and memory footprint required for neuronal translation of speech.
ON-TRAC will allow the processing of three pairs of distinct languages with increasing operational, security and defense interest and difficulty (English-French, French-Pashto, French-Tamacheq).
The ON-TRAC project is part of Axis 4 “Data, Knowledge, Big Data, Multimedia Content, Artificial Intelligence” of Challenge 7 “Information and Communication Society” of the 2018 Action Plan of the ANR.
By its main scientific theme dedicated to the translation of speech through end-to-end neural approaches, it is clearly positioned in the themes ” Data to knowledge ” and ” Treatment of multimedia content ”.