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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 Hits 1 – 20 of 62 1 Applying phonetics : speech science in everyday life Munro, Murray J.. - Chichester, West Sussex : Wiley Blackwell, 2021 BLLDB UB Frankfurt Linguistik Show details 2 Automatic Speech Recognition systems errors for accident-prone sleepiness detection through voice Martin, Vincent,; Rouas, Jean-Luc; Boyer, Florian... In: EUSIPCO 2021 ; https://hal.archives-ouvertes.fr/hal-03324033 ; EUSIPCO 2021, Aug 2021, Dublin (en ligne), Ireland. ⟨10.23919/EUSIPCO54536.2021.9616299⟩ (2021) BASE Show details 3 Automatic Speech Recognition systems errors for objective sleepiness detection through voice Martin, Vincent,; Rouas, Jean-Luc; Boyer, Florian... In: Proceedings Interspeech 2021 ; Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03328827 ; Interspeech 2021, Aug 2021, Brno (virtual), Czech Republic. pp.2476-2480, ⟨10.21437/Interspeech.2021-291⟩ (2021) BASE Show details 4 LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech Evain, Solène; Nguyen, Ha; Le, Hang... In: INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021) BASE Show details 5 LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech Evain, Solène; Nguyen, Ha; Le, Hang... In: INTERSPEECH 2021: ; INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021) BASE Show details 6 LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech Evain, Solène; Nguyen, Ha; Le, Hang... In: INTERSPEECH 2021: ; INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021) BASE Show details 7 Re-synchronization using the Hand Preceding Model for Multi-modal Fusion in Automatic Continuous Cued Speech Recognition Liu, Li; Feng, Gang; Beautemps, Denis... In: ISSN: 1520-9210 ; IEEE Transactions on Multimedia ; https://hal.archives-ouvertes.fr/hal-02433830 ; IEEE Transactions on Multimedia, Institute of Electrical and Electronics Engineers, 2021, 23, pp.292-305. ⟨10.1109/TMM.2020.2976493⟩ (2021) BASE Show details 8 Identifying Speaker State from Multimodal Cues Yang, Zixiaofan. - 2021 BASE Show details 9 Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios Eskander, Ramy. - 2021 Abstract: With the high cost of manually labeling data and the increasing interest in low-resource languages, for which human annotators might not be even available, unsupervised approaches have become essential for processing a typologically diverse set of languages, whether high-resource or low-resource. In this work, we propose new fully unsupervised approaches for two tasks in morphology: unsupervised morphological segmentation and unsupervised cross-lingual part-of-speech (POS) tagging, which have been two essential subtasks for several downstream NLP applications, such as machine translation, speech recognition, information extraction and question answering. We propose a new unsupervised morphological-segmentation approach that utilizes Adaptor Grammars (AGs), nonparametric Bayesian models that generalize probabilistic context-free grammars (PCFGs), where a PCFG models word structure in the task of morphological segmentation. We implement the approach as a publicly available morphological-segmentation framework, MorphAGram, that enables unsupervised morphological segmentation through the use of several proposed language-independent grammars. In addition, the framework allows for the use of scholar knowledge, when available, in the form of affixes that can be seeded into the grammars. The framework handles the cases when the scholar-seeded knowledge is either generated from language resources, possibly by someone who does not know the language, as weak linguistic priors, or generated by an expert in the underlying language as strong linguistic priors. Another form of linguistic priors is the design of a grammar that models language-dependent specifications. We also propose a fully unsupervised learning setting that approximates the effect of scholar-seeded knowledge through self-training. Moreover, since there is no single grammar that works best across all languages, we propose an approach that picks a nearly optimal configuration (a learning setting and a grammar) for an unseen language, a language that is not part of the development. Finally, we examine multilingual learning for unsupervised morphological segmentation in low-resource setups. For unsupervised POS tagging, two cross-lingual approaches have been widely adapted: 1) annotation projection, where POS annotations are projected across an aligned parallel text from a source language for which a POS tagger is accessible to the target one prior to training a POS model; and 2) zero-shot model transfer, where a model of a source language is directly applied on texts in the target language. We propose an end-to-end architecture for unsupervised cross-lingual POS tagging via annotation projection in truly low-resource scenarios that do not assume access to parallel corpora that are large in size or represent a specific domain. We integrate and expand the best practices in alignment and projection and design a rich neural architecture that exploits non-contextualized and transformer-based contextualized word embeddings, affix embeddings and word-cluster embeddings. Additionally, since parallel data might be available between the target language and multiple source ones, as in the case of the Bible, we propose different approaches for learning from multiple sources. Finally, we combine our work on unsupervised morphological segmentation and unsupervised cross-lingual POS tagging by conducting unsupervised stem-based cross-lingual POS tagging via annotation projection, which relies on the stem as the core unit of abstraction for alignment and projection, which is beneficial to low-resource morphologically complex languages. We also examine morpheme-based alignment and projection, the use of linguistic priors towards better POS models and the use of segmentation information as learning features in the neural architecture. We conduct comprehensive evaluation and analysis to assess the performance of our approaches of unsupervised morphological segmentation and unsupervised POS tagging and show that they achieve the state-of-the-art performance for the two morphology tasks when evaluated on a large set of languages of different typologies: analytic, fusional, agglutinative and synthetic/polysynthetic. Keyword: Automatic speech recognition--Computer programs; Computer science; Machine translating; Question-answering systems; Speech processing systems--Computer programs URL: https://doi.org/10.7916/d8-jd2d-9p51 BASE Hide details 10 Recognizing lexical units in low-resource language contexts with supervised and unsupervised neural networks MACAIRE, Cécile In: https://hal.archives-ouvertes.fr/hal-03429051 ; [Research Report] LACITO (UMR 7107). 2021 (2021) BASE Show details 11 Speech Normalization and Data Augmentation Techniques Based on Acoustical and Physiological Constraints and Their Applications to Child Speech Recognition Yeung, Gary Joseph. - : eScholarship, University of California, 2021 BASE Show details 12 Large vocabulary automatic speech recognition: from hybrid to end-to-end approaches ; Reconnaissance automatique de la parole à large vocabulaire : des approches hybrides aux approches End-to-End Heba, Abdelwahab. - : HAL CCSD, 2021 In: https://hal.archives-ouvertes.fr/tel-03269807 ; Son [cs.SD]. Université toulouse 3 Paul Sabatier, 2021. Français (2021) BASE Show details 13 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 14 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 15 The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021) BASE Show details 16 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 17 The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021) BASE Show details 18 Enhancing Speech Privacy with Slicing Maouche, Mohamed; Srivastava, Brij Mohan Lal; Vauquier, Nathalie... In: https://hal.inria.fr/hal-03369137 ; 2021 (2021) BASE Show details 19 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 20 Training RNN Language Models on Uncertain ASR Hypotheses in Limited Data Scenarios Sheikh, Imran,; Vincent, Emmanuel; Illina, Irina In: https://hal.inria.fr/hal-03327306 ; 2021 (2021) BASE Show details Page: 1 2 3 4

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