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Segmentation en mots faiblement supervisée pour la documentation automatique des langues
In: https://hal.archives-ouvertes.fr/hal-03477475 ; 2021 (2021)
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2
Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads? ...
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3
Lightweight Adapter Tuning for Multilingual Speech Translation ...
Le, Hang; Pino, Juan; Wang, Changhan. - : arXiv, 2021
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4
Multilingual Unsupervised Neural Machine Translation with Denoising Adapters ...
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5
Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings ...
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6
User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis
In: ComputEL-4: Fourth Workshop on the Use of Computational Methods in the Study of Endangered Languages ; https://halshs.archives-ouvertes.fr/halshs-03030529 ; 2020 ; https://computel-workshop.org/ (2020)
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7
A Data Efficient End-To-End Spoken Language Understanding Architecture ...
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8
Catplayinginthesnow: Impact of Prior Segmentation on a Model of Visually Grounded Speech ...
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9
Investigating Language Impact in Bilingual Approaches for Computational Language Documentation ...
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10
Controlling Utterance Length in NMT-based Word Segmentation with Attention ...
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11
MaSS - Multilingual corpus of Sentence-aligned Spoken utterances ...
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MaSS - Multilingual corpus of Sentence-aligned Spoken utterances ...
Abstract: Abstract The CMU Wilderness Multilingual Speech Dataset is a newly published multilingual speech dataset based on recorded readings of the New Testament. It provides data to build Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) models for potentially 700 languages. However, the fact that the source content (the Bible), is the same for all the languages is not exploited to date. Therefore, this article proposes to add multilingual links between speech segments in different languages, and shares a large and clean dataset of 8,130 para-lel spoken utterances across 8 languages (56 language pairs).We name this corpus MaSS (Multilingual corpus of Sentence-aligned Spoken utterances). The covered languages (Basque, English, Finnish, French, Hungarian, Romanian, Russian and Spanish) allow researches on speech-to-speech alignment as well as on translation for syntactically divergent language pairs. The quality of the final corpus is attested by human evaluation performed on a corpus subset (100 utterances, ...
Keyword: parallel speech corpus, multilingual alignment, speech-to-speech alignment, speech-to-speech translation
URL: https://dx.doi.org/10.5281/zenodo.3354711
https://zenodo.org/record/3354711
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13
How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages ...
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14
Word Recognition, Competition, and Activation in a Model of Visually Grounded Speech ...
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15
Models of Visually Grounded Speech Signal Pay Attention To Nouns: a Bilingual Experiment on English and Japanese ...
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16
Synthetically Spoken STAIR ...
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17
Synthetically Spoken STAIR ...
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18
Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop ...
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19
Unsupervised Word Segmentation from Speech with Attention ...
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SPEECH-COCO ...
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