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Make More of Your Data: Minimal Effort Data Augmentation for Automatic
  Speech Recognition and Translation
v1v2 (latest)

Make More of Your Data: Minimal Effort Data Augmentation for Automatic Speech Recognition and Translation

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2022
27 October 2022
Tsz Kin Lam
Shigehiko Schamoni
Stefan Riezler
    VLM
ArXiv (abs)PDFHTML

Papers citing "Make More of Your Data: Minimal Effort Data Augmentation for Automatic Speech Recognition and Translation"

3 / 3 papers shown
Title
Prepending or Cross-Attention for Speech-to-Text? An Empirical Comparison
Prepending or Cross-Attention for Speech-to-Text? An Empirical ComparisonNorth American Chapter of the Association for Computational Linguistics (NAACL), 2025
Tsz Kin Lam
Marco Gaido
Sara Papi
L. Bentivogli
Barry Haddow
397
3
0
04 Jan 2025
Joint Speech Transcription and Translation: Pseudo-Labeling with
  Out-of-Distribution Data
Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution DataAnnual Meeting of the Association for Computational Linguistics (ACL), 2022
Mozhdeh Gheini
Tatiana Likhomanenko
Matthias Sperber
Hendra Setiawan
159
7
0
20 Dec 2022
SegAugment: Maximizing the Utility of Speech Translation Data with
  Segmentation-based Augmentations
SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based AugmentationsConference on Empirical Methods in Natural Language Processing (EMNLP), 2022
Ioannis Tsiamas
José A. R. Fonollosa
Marta R. Costa-jussá
210
6
0
19 Dec 2022
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