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1
How2Sign: A large-scale multimodal dataset for continuous American sign language
Duarte, Amanda; Palaskar, Shruti; Ventura, Lucas. - : Institute of Electrical and Electronics Engineers, 2021
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2
Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models ...
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3
Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models ...
NAACL 2021 2021; Hauptmann, Alexander; Hu, Junjie. - : Underline Science Inc., 2021
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4
Differentiable Allophone Graphs for Language-Universal Speech Recognition ...
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Speech technology for unwritten languages
In: ISSN: 2329-9290 ; EISSN: 2329-9304 ; IEEE/ACM Transactions on Audio, Speech and Language Processing ; https://hal.inria.fr/hal-02480675 ; IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2020, ⟨10.1109/TASLP.2020.2973896⟩ (2020)
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AlloVera: a multilingual allophone database
In: LREC 2020: 12th Language Resources and Evaluation Conference ; https://halshs.archives-ouvertes.fr/halshs-02527046 ; LREC 2020: 12th Language Resources and Evaluation Conference, European Language Resources Association, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/ (2020)
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AlloVera: A Multilingual Allophone Database ...
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8
Towards Zero-shot Learning for Automatic Phonemic Transcription ...
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9
How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language ...
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10
Universal Phone Recognition with a Multilingual Allophone System ...
Abstract: Multilingual models can improve language processing, particularly for low resource situations, by sharing parameters across languages. Multilingual acoustic models, however, generally ignore the difference between phonemes (sounds that can support lexical contrasts in a particular language) and their corresponding phones (the sounds that are actually spoken, which are language independent). This can lead to performance degradation when combining a variety of training languages, as identically annotated phonemes can actually correspond to several different underlying phonetic realizations. In this work, we propose a joint model of both language-independent phone and language-dependent phoneme distributions. In multilingual ASR experiments over 11 languages, we find that this model improves testing performance by 2% phoneme error rate absolute in low-resource conditions. Additionally, because we are explicitly modeling language-independent phones, we can build a (nearly-)universal phone recognizer that, when ... : ICASSP 2020 ...
Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD
URL: https://arxiv.org/abs/2002.11800
https://dx.doi.org/10.48550/arxiv.2002.11800
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11
AlloVera: a multilingual allophone database
In: LREC 2020: 12th Language Resources and Evaluation Conference ; https://halshs.archives-ouvertes.fr/halshs-02527046 ; LREC 2020: 12th Language Resources and Evaluation Conference, European Language Resources Association, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/ (2020)
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12
Phoneme Level Language Models for Sequence Based Low Resource ASR ...
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13
Multilingual Speech Recognition with Corpus Relatedness Sampling ...
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14
On Leveraging the Visual Modality for Neural Machine Translation ...
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15
Acoustic-to-Word Models with Conversational Context Information ...
Kim, Suyoun; Metze, Florian. - : arXiv, 2019
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16
Learned In Speech Recognition: Contextual Acoustic Word Embeddings ...
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17
On Dimensional Linguistic Properties of the Word Embedding Space ...
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18
Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the “Speaking rosetta” JSALT 2017 workshop
In: ICASSP 2018 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-01709578 ; ICASSP 2018 - IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2018, Calgary, Alberta, Canada (2018)
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19
Late fusion of individual engines for improved recognition of negative emotion in speech - learning vs. democratic vote ...
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20
Sequence-based Multi-lingual Low Resource Speech Recognition ...
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