1 |
Controlling Utterance Length in NMT-based Word Segmentation with Attention
|
|
|
|
In: International Workshop on Spoken Language Translation ; https://hal.archives-ouvertes.fr/hal-02343206 ; International Workshop on Spoken Language Translation, Nov 2019, Hong-Kong, China (2019)
|
|
BASE
|
|
Show details
|
|
2 |
Empirical Evaluation of Sequence-to-Sequence Models for Word Discovery in Low-resource Settings
|
|
|
|
In: Interspeech 2019 ; https://hal.archives-ouvertes.fr/hal-02193867 ; Interspeech 2019, Sep 2019, Graz, Austria (2019)
|
|
BASE
|
|
Show details
|
|
3 |
Word Recognition, Competition, and Activation in a Model of Visually Grounded Speech
|
|
|
|
In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL) ; https://hal.archives-ouvertes.fr/hal-02359540 ; Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), Nov 2019, Hong Kong, China. pp.339-348, ⟨10.18653/v1/K19-1032⟩ (2019)
|
|
BASE
|
|
Show details
|
|
4 |
The Zero Resource Speech Challenge 2019: TTS without T
|
|
|
|
In: Interspeech 2019 - 20th Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-02274112 ; Interspeech 2019 - 20th Annual Conference of the International Speech Communication Association, Sep 2019, Graz, Austria (2019)
|
|
BASE
|
|
Show details
|
|
5 |
Models of Visually Grounded Speech Signal Pay Attention to Nouns: A Bilingual Experiment on English and Japanese
|
|
|
|
In: International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; https://hal.archives-ouvertes.fr/hal-02013984 ; International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019, Brighton, United Kingdom. pp.8618-8622, ⟨10.1109/ICASSP.2019.8683069⟩ (2019)
|
|
BASE
|
|
Show details
|
|
6 |
A neural approach for inducing multilingual resources and natural language processing tools for low-resource languages
|
|
|
|
In: ISSN: 1351-3249 ; EISSN: 1469-8110 ; Natural Language Engineering ; https://hal.archives-ouvertes.fr/hal-01976297 ; Natural Language Engineering, Cambridge University Press (CUP), 2019, 25 (01), pp.43-67. ⟨10.1017/S1351324918000293⟩ (2019)
|
|
BASE
|
|
Show details
|
|
7 |
How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages
|
|
|
|
In: Journées Scientifiques du Groupement de Recherche: Linguistique Informatique, Formelle et de Terrain (LIFT). ; https://hal.archives-ouvertes.fr/hal-02895895 ; Journées Scientifiques du Groupement de Recherche: Linguistique Informatique, Formelle et de Terrain (LIFT)., Nov 2019, Orléans, France (2019)
|
|
BASE
|
|
Show details
|
|
8 |
Controlling Utterance Length in NMT-based Word Segmentation with Attention ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Controlling Utterance Length in NMT-based Word Segmentation with Attention ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Controlling Utterance Length in NMT-based Word Segmentation with Attention ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
MaSS - Multilingual corpus of Sentence-aligned Spoken utterances ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
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
|
|
BASE
|
|
Hide details
|
|
13 |
How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Word Recognition, Competition, and Activation in a Model of Visually Grounded Speech ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Models of Visually Grounded Speech Signal Pay Attention To Nouns: a Bilingual Experiment on English and Japanese ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|