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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 Hits 1 – 14 of 14 1 Pseudo-Labeling for Massively Multilingual Speech Recognition ... Lugosch, Loren; Likhomanenko, Tatiana; Synnaeve, Gabriel. - : arXiv, 2021 BASE Show details 2 LIBRI-LIGHT: a benchmark for asr with limited or no supervision Kahn, Jacob; Rivière, Morgane; Zheng, Weiyi... In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-02959460 ; ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2020, Barcelona / Virtual, Spain. pp.7669-7673, ⟨10.1109/ICASSP40776.2020.9052942⟩ (2020) BASE Show details 3 Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters ... Pratap, Vineel; Sriram, Anuroop; Tomasello, Paden. - : arXiv, 2020 BASE Show details 4 MLS: A Large-Scale Multilingual Dataset for Speech Research ... Pratap, Vineel; Xu, Qiantong; Sriram, Anuroop. - : arXiv, 2020 BASE Show details 5 Unsupervised Cross-lingual Representation Learning for Speech Recognition ... Conneau, Alexis; Baevski, Alexei; Collobert, Ronan. - : arXiv, 2020 BASE Show details 6 End-to-End Acoustic Modeling using Convolutional Neural Networks for HMM-based Automatic Speech Recognition Collobert, Ronan; Magimai.-Doss, Mathew; Palaz, Dimitri In: http://infoscience.epfl.ch/record/264125 (2019) BASE Show details 7 End-to-End Speech Recognition From the Raw Waveform Zeghidour, Neil; Usunier, Nicolas; Synnaeve, Gabriel... In: Interspeech 2018 ; https://hal.archives-ouvertes.fr/hal-01888739 ; Interspeech 2018, Sep 2018, Hyderabad, India. ⟨10.21437/Interspeech.2018-2414⟩ (2018) BASE Show details 8 Fully Convolutional Speech Recognition ... Zeghidour, Neil; Xu, Qiantong; Liptchinsky, Vitaliy. - : arXiv, 2018 BASE Show details 9 Learning linearly separable features for speech recognition using convolutional neural networks ... Palaz, Dimitri; Doss, Mathew Magimai; Collobert, Ronan. - : arXiv, 2014 BASE Show details 10 Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks Magimai.-Doss, Mathew; Collobert, Ronan; Palaz, Dimitri In: http://infoscience.epfl.ch/record/192756 (2013) BASE Show details 11 Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks Palaz, Dimitri; Magimai.-Doss, Mathew; Collobert, Ronan In: http://infoscience.epfl.ch/record/192560 (2013) BASE Show details 12 Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks ... Palaz, Dimitri; Collobert, Ronan; -Doss, Mathew Magimai.. - : arXiv, 2013 BASE Show details 13 Towards Understanding Situated Natural Language Bordes, Antoine; Usunier, Nicolas; Collobert, Ronan... In: 13th International Conference on Artificial Intelligence and Statistics ; https://hal.archives-ouvertes.fr/hal-00750937 ; 13th International Conference on Artificial Intelligence and Statistics, May 2010, Chia Laguna Resort, Sardinia, Italy. pp.65-72 (2010) BASE Show details 14 Large Scale Application of Neural Network Based Semantic Role Labeling for Automated Relation Extraction from Biomedical Texts Barnickel, Thorsten; Weston, Jason; Collobert, Ronan; Mewes, Hans-Werner; Stümpflen, Volker. - : Public Library of Science, 2009 Abstract: To reduce the increasing amount of time spent on literature search in the life sciences, several methods for automated knowledge extraction have been developed. Co-occurrence based approaches can deal with large text corpora like MEDLINE in an acceptable time but are not able to extract any specific type of semantic relation. Semantic relation extraction methods based on syntax trees, on the other hand, are computationally expensive and the interpretation of the generated trees is difficult. Several natural language processing (NLP) approaches for the biomedical domain exist focusing specifically on the detection of a limited set of relation types. For systems biology, generic approaches for the detection of a multitude of relation types which in addition are able to process large text corpora are needed but the number of systems meeting both requirements is very limited. We introduce the use of SENNA (“Semantic Extraction using a Neural Network Architecture”), a fast and accurate neural network based Semantic Role Labeling (SRL) program, for the large scale extraction of semantic relations from the biomedical literature. A comparison of processing times of SENNA and other SRL systems or syntactical parsers used in the biomedical domain revealed that SENNA is the fastest Proposition Bank (PropBank) conforming SRL program currently available. 89 million biomedical sentences were tagged with SENNA on a 100 node cluster within three days. The accuracy of the presented relation extraction approach was evaluated on two test sets of annotated sentences resulting in precision/recall values of 0.71/0.43. We show that the accuracy as well as processing speed of the proposed semantic relation extraction approach is sufficient for its large scale application on biomedical text. The proposed approach is highly generalizable regarding the supported relation types and appears to be especially suited for general-purpose, broad-scale text mining systems. The presented approach bridges the gap between fast, cooccurrence-based approaches lacking semantic relations and highly specialized and computationally demanding NLP approaches. Keyword: Research Article URL: http://www.ncbi.nlm.nih.gov/pubmed/19636432 https://doi.org/10.1371/journal.pone.0006393 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2712690 BASE Hide details

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