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pygamma-agreement: Gamma γ measure for inter/intra-annotator agreement in Python ; pygamma-agreement: La mesure γ pour l'accord inter/intra-annotateurs en Python
In: https://hal.archives-ouvertes.fr/hal-03144116 ; 2021 (2021)
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pygamma-agreement: Gamma γ measure for inter/intra-annotator agreement in Python ; pygamma-agreement: La mesure γ pour l'accord inter/intra-annotateurs en Python
In: https://hal.archives-ouvertes.fr/hal-03144116 ; 2021 (2021)
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3
Learning spectro-temporal representations of complex sounds with parameterized neural networks
In: ISSN: 0001-4966 ; EISSN: 1520-8524 ; Journal of the Acoustical Society of America ; https://hal.inria.fr/hal-03329261 ; Journal of the Acoustical Society of America, Acoustical Society of America, 2021, 150 (1), pp.353-366. ⟨10.1121/10.0005482⟩ (2021)
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4
pygamma-agreement: Gamma measure for inter/intra-annotator agreement in Python ...
Titeux, Hadrien; Riad, Rachid. - : Zenodo, 2021
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pygamma-agreement: Gamma measure for inter/intra-annotator agreement in Python ...
Titeux, Hadrien; Riad, Rachid. - : Zenodo, 2021
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6
Seshat: A tool for managing and verifying annotation campaigns of audio data
In: LREC 2020 - 12th Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02496041 ; LREC 2020 - 12th Language Resources and Evaluation Conference, May 2020, Marseille, France. pp.6976-6982 (2020)
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7
Vocal markers from sustained phonation in Huntington's Disease
In: INTERSPEECH 2020 - Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03070388 ; INTERSPEECH 2020 - Annual Conference of the International Speech Communication Association, Oct 2020, Shanghai / Virtual, China (2020)
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8
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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9
Longform recordings : Opportunities and challenges ; Enregistrements de longue durée: Opportunités et défis
In: Actes des 2èmes journées scientifiques du Groupement de Recherche Linguistique Informatique Formelle et de Terrain (LIFT). ; LIFT 2020 - 2èmes journées scientifiques du Groupement de Recherche "Linguistique informatique, formelle et de terrain" ; https://hal.archives-ouvertes.fr/hal-03047153 ; LIFT 2020 - 2èmes journées scientifiques du Groupement de Recherche "Linguistique informatique, formelle et de terrain", Dec 2020, Montrouge / Virtual, France. pp.64-71 (2020)
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10
Identification of primary and collateral tracks in stuttered speech
In: LREC 2020 - 12th Conference on Language Resources and Evaluation ; https://hal.archives-ouvertes.fr/hal-02959454 ; LREC 2020 - 12th Conference on Language Resources and Evaluation, May 2020, Marseille, France (2020)
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11
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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12
Learning Word Embeddings: Unsupervised Methods for Fixed-size Representations of Variable-length Speech Segments
In: Interspeech 2018 ; https://hal.archives-ouvertes.fr/hal-01888708 ; Interspeech 2018, Sep 2018, Hyderabad, India. ⟨10.21437/Interspeech.2018-2364⟩ (2018)
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13
Sampling strategies in Siamese Networks for unsupervised speech representation learning
In: Interspeech 2018 ; https://hal.archives-ouvertes.fr/hal-01888725 ; Interspeech 2018, Sep 2018, Hyderabad, India (2018)
Abstract: Conference paper at Interspeech 2018 ; International audience ; Recent studies have investigated siamese network architectures for learning invariant speech representations using same-different side information at the word level. Here we investigate systematically an often ignored component of siamese networks: the sampling procedure (how pairs of same vs. different tokens are selected). We show that sampling strategies taking into account Zipf's Law, the distribution of speakers and the proportions of same and different pairs of words significantly impact the performance of the network. In particular, we show that word frequency compression improves learning across a large range of variations in number of training pairs. This effect does not apply to the same extent to the fully unsupervised setting, where the pairs of same-different words are obtained by spoken term discovery. We apply these results to pairs of words discovered using an unsupervised algorithm and show an improvement on state-of-the-art in unsupervised representation learning using siamese networks.
Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [SCCO.LING]Cognitive science/Linguistics; [SCCO]Cognitive science; [STAT.OT]Statistics [stat]/Other Statistics [stat.ML]; ABX; Index Terms: language acquisition; Language acquisition; sam-pling; Sampling; Siamese network; speech embeddings; speech recognition; unsupervised learning; weakly supervised learning; zero re-source speech technology; Zero resource speech technology; Zipf's law; Zipf’s law
URL: https://hal.archives-ouvertes.fr/hal-01888725/document
https://hal.archives-ouvertes.fr/hal-01888725
https://hal.archives-ouvertes.fr/hal-01888725/file/Riad_DKZSD_2018_Sampling_strategies_for_unsup_siamese.Interspeech.pdf
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Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop ...
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