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        <title>Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation</title>
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        <namePart type="given">Elizabeth</namePart>
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            <namePart type="given">Anna</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>Previous work on end-to-end translation from speech has primarily used frame-level features as speech representations, which creates longer, sparser sequences than text. We show that a naive method to create compressed phoneme-like speech representations is far more effective and efficient for translation than traditional frame-level speech features. Specifically, we generate phoneme labels for speech frames and average consecutive frames with the same label to create shorter, higher-level source sequences for translation. We see improvements of up to 5 BLEU on both our high and low resource language pairs, with a reduction in training time of 60%. Our improvements hold across multiple data sizes and two language pairs.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1179</identifier>
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%0 Conference Proceedings
%T Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation
%A Salesky, Elizabeth
%A Sperber, Matthias
%A Black, Alan W.
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F salesky-etal-2019-exploring
%X Previous work on end-to-end translation from speech has primarily used frame-level features as speech representations, which creates longer, sparser sequences than text. We show that a naive method to create compressed phoneme-like speech representations is far more effective and efficient for translation than traditional frame-level speech features. Specifically, we generate phoneme labels for speech frames and average consecutive frames with the same label to create shorter, higher-level source sequences for translation. We see improvements of up to 5 BLEU on both our high and low resource language pairs, with a reduction in training time of 60%. Our improvements hold across multiple data sizes and two language pairs.
%R 10.18653/v1/P19-1179
%U https://aclanthology.org/P19-1179
%U https://doi.org/10.18653/v1/P19-1179
%P 1835-1841

Markdown (Informal)

[Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation](https://aclanthology.org/P19-1179) (Salesky et al., ACL 2019)

• Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation (Salesky et al., ACL 2019)

ACL

• Elizabeth Salesky, Matthias Sperber, and Alan W Black. 2019. Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1835–1841, Florence, Italy. Association for Computational Linguistics.