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Using Automatic Speech Recognition to Optimize Hearing-Aid Time Constants
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In: ISSN: 1662-4548 ; EISSN: 1662-453X ; Frontiers in Neuroscience ; https://hal.archives-ouvertes.fr/hal-03627441 ; Frontiers in Neuroscience, Frontiers, 2022, 16 (779062), ⟨10.3389/fnins.2022.779062⟩ ; https://www.frontiersin.org/articles/10.3389/fnins.2022.779062/full (2022)
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RETRIEVING SPEAKER INFORMATION FROM PERSONALIZED ACOUSTIC MODELS FOR SPEECH RECOGNITION
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In: IEEE ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03539741 ; IEEE ICASSP 2022, 2022, Singapour, Singapore (2022)
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Emotional Speech Recognition Using Deep Neural Networks
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In: ISSN: 1424-8220 ; Sensors ; https://hal.archives-ouvertes.fr/hal-03632853 ; Sensors, MDPI, 2022, 22 (4), pp.1414. ⟨10.3390/s22041414⟩ (2022)
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The Impact of Removing Head Movements on Audio-visual Speech Enhancement
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In: ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.inria.fr/hal-03551610 ; ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE Signal Processing Society, May 2022, Singapore, Singapore. pp.1-5 (2022)
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Efficient localization of the cortical language network and its functional neuroanatomy in dyslexia
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How are visemes and graphemes integrated with speech sounds during spoken word recognition? ERP evidence for supra-additive responses during audiovisual compared to auditory speech processing
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In: ISSN: 0093-934X ; EISSN: 1090-2155 ; Brain and Language ; https://hal.archives-ouvertes.fr/hal-03472191 ; Brain and Language, Elsevier, 2022, 225, ⟨10.1016/j.bandl.2021.105058⟩ (2022)
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Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding
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In: ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-03578503 ; ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2022, Singapour, Singapore (2022)
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| 11 |
Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding
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In: ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-03578503 ; ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2022, Singapour, Singapore (2022)
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| 12 |
Hippocampal and auditory contributions to speech segmentation
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In: ISSN: 0010-9452 ; Cortex ; https://hal.archives-ouvertes.fr/hal-03604957 ; Cortex, Elsevier, 2022, ⟨10.1016/j.cortex.2022.01.017⟩ (2022)
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| 13 |
Speech Perception and Implementation in a Virtual Medical Assistant
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In: 6. ICAART – 14th International Conference on Agents and Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-03621550 ; 6. ICAART – 14th International Conference on Agents and Artificial Intelligence, Feb 2022, Vienna, Austria (2022)
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| 14 |
Évaluation de la perception des sons de parole chez les populations pédiatriques : réflexion sur les épreuves existantes
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In: ISSN: 0298-6477 ; EISSN: 2117-7155 ; Glossa ; https://hal.archives-ouvertes.fr/hal-03646757 ; Glossa, UNADREO - Union NAtionale pour le Développement de la Recherche en Orthophonie, 2022, 132, pp.1-27 ; https://www.glossa.fr/index.php/glossa/article/view/1043 (2022)
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| 15 |
Automatic generation of the complete vocal tract shape from the sequence of phonemes to be articulated
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In: ISSN: 0167-6393 ; EISSN: 1872-7182 ; Speech Communication ; https://hal.univ-lorraine.fr/hal-03650212 ; Speech Communication, Elsevier : North-Holland, 2022, ⟨10.1016/j.specom.2022.04.004⟩ (2022)
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Cross-lingual few-shot hate speech and offensive language detection using meta learning
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In: ISSN: 2169-3536 ; EISSN: 2169-3536 ; IEEE Access ; https://hal.archives-ouvertes.fr/hal-03559484 ; IEEE Access, IEEE, 2022, 10, pp.14880-14896. ⟨10.1109/ACCESS.2022.3147588⟩ (2022)
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Abstract:
International audience ; Automatic detection of abusive online content such as hate speech, offensive language, threats, etc. has become prevalent in social media, with multiple efforts dedicated to detecting this phenomenon in English. However, detecting hatred and abuse in low-resource languages is a non-trivial challenge. The lack of sufficient labeled data in low-resource languages and inconsistent generalization ability of transformer-based multilingual pre-trained language models for typologically diverse languages make these models inefficient in some cases. We propose a meta learning-based approach to study the problem of few-shot hate speech and offensive language detection in low-resource languages that will allow hateful or offensive content to be predicted by only observing a few labeled data items in a specific target language. We investigate the feasibility of applying a meta learning approach in cross-lingual few-shot hate speech detection by leveraging two meta learning models based on optimization-based and metric-based (MAML and Proto-MAML) methods. To the best of our knowledge, this is the first effort of this kind. To evaluate the performance of our approach, we consider hate speech and offensive language detection as two separate tasks and make two diverse collections of different publicly available datasets comprising 15 datasets across 8 languages for hate speech and 6 datasets across 6 languages for offensive language. Our experiments show that meta learning-based models outperform transfer learning-based models in a majority of cases, and that Proto-MAML is the best performing model, as it can quickly generalize and adapt to new languages with only a few labeled data points (generally, 16 samples per class yields an effective performance) to identify hateful or offensive content.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]; [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]; Cross-lingual classification; Few-shot learning; Hate speech; Meta learning; Offensive language; Transfer learning; XLMRoBERTa
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URL: https://doi.org/10.1109/ACCESS.2022.3147588
https://hal.archives-ouvertes.fr/hal-03559484
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| 17 |
Fine-tuning pre-trained models for Automatic Speech Recognition: experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)
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In: https://halshs.archives-ouvertes.fr/halshs-03647315 ; 2022 (2022)
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Intelligibility and comprehensibility: A Delphi consensus study
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In: ISSN: 1368-2822 ; EISSN: 1460-6984 ; International Journal of Language and Communication Disorders ; https://hal.archives-ouvertes.fr/hal-03543198 ; International Journal of Language and Communication Disorders, Wiley, 2022, 57 (1), pp.21 - 41. ⟨10.1111/1460-6984.12672⟩ ; https://onlinelibrary.wiley.com/doi/10.1111/1460-6984.12672 (2022)
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Vocal size exaggeration may have contributed to the origins of vocalic complexity
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In: ISSN: 0962-8436 ; EISSN: 1471-2970 ; Philosophical Transactions of the Royal Society B: Biological Sciences ; https://hal.archives-ouvertes.fr/hal-03501105 ; Philosophical Transactions of the Royal Society B: Biological Sciences, Royal Society, The, 2022, 377 (1841), ⟨10.1098/rstb.2020.0401⟩ (2022)
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Investigating the locus of transposed-phoneme effects using cross-modal priming
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In: ISSN: 0001-6918 ; EISSN: 1873-6297 ; Acta Psychologica ; https://hal.archives-ouvertes.fr/hal-03619856 ; Acta Psychologica, Elsevier, 2022, 226, pp.103578. ⟨10.1016/j.actpsy.2022.103578⟩ (2022)
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