Towards speech-to-text translation without speech recognition

Sameer Bansal, Herman Kamper, Adam Lopez, Sharon Goldwater

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We explore the problem of translating speech to text in low-resource scenarios where neither automatic speech recognition (ASR) nor machine translation (MT) are available, but we have training data in the form of audio paired with text translations. We present the first system for this problem applied to a realistic multi-speaker dataset, the CALLHOME Spanish-English speech translation corpus. Our approach uses unsupervised term discovery (UTD) to cluster repeated patterns in the audio, creating a pseudotext, which we pair with translations to create a parallel text and train a simple bag-of-words MT model. We identify the challenges faced by the system, finding that the difficulty of cross-speaker UTD results in low recall, but that our system is still able to correctly translate some content words in test data.
Original languageEnglish
Title of host publicationProceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Place of PublicationValencia, Spain
PublisherAssociation for Computational Linguistics (ACL)
Pages474-479
Number of pages6
ISBN (Print)978-1-945626-34-0
Publication statusPublished - 7 Apr 2017
Event15th EACL 2017 Software Demonstrations - Valencia, Spain
Duration: 3 Apr 20177 Apr 2017
http://eacl2017.org/
http://eacl2017.org/index.php

Conference

Conference15th EACL 2017 Software Demonstrations
Abbreviated titleEACL 2017
Country/TerritorySpain
CityValencia
Period3/04/177/04/17
Internet address

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