Speech technology for unwritten languages
Odette Scharenborg (Radboud Universiteit Nijmegen, TU Delft - Multimedia Computing)
Laurent Besacier (LIG)
Alan W. Black (Carnegie Mellon University)
Mark Hasegawa-Johnson (University of Illinois at Urbana Champaign)
Florian Metze (Carnegie Mellon University)
Graham Neubig (Carnegie Mellon University)
Sebastian Stueker (Karlsruhe Institut für Technologie)
Pierre Godard (LIMSI, ele-de-France)
M Mueller (Karlsruhe Institut für Technologie)
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Abstract
Speech technology plays an important role in our everyday life. Among others, speech is used for human-computer interaction, for instance for information retrieval and on-line shopping. In the case of an unwritten language, however, speech technology is unfortunately difficult to create, because it cannot be created by the standard combination of pre-trained speech-to-text and text-to-speech subsystems. The research presented in this article takes the first steps towards speech technology for unwritten languages. Specifically, the aim of this work was 1) to learn speech-to-meaning representations without using text as an intermediate representation, and 2) to test the sufficiency of the learned representations to regenerate speech or translated text, or to retrieve images that depict the meaning of an utterance in an unwritten language. The results suggest that building systems that go directly from speech-to-meaning and from meaning-to-speech, bypassing the need for text, is possible.