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Found 26 papers, 6 papers with code
Identification des Expressions Polylexicales dans les Tweets (Identification of Multiword Expressions in Tweets)
L’identification des expressions polylexicales (EP) dans les tweets est une tâche difficile en raison de la nature linguistique complexe des EP combinée à l’utilisation d’un langage non standard.
Generalisability of Topic Models in Cross-corpora Abusive Language Detection
Rapidly changing social media content calls for robust and generalisable abuse detection models.
Unsupervised Domain Adaptation in Cross-corpora Abusive Language Detection
The state-of-the-art abusive language detection models report great in-corpus performance, but underperform when evaluated on abusive comments that differ from the training scenario.
Transformer versus LSTM Language Models trained on Uncertain ASR Hypotheses in Limited Data Scenarios
Training of LSTM LMs in such limited data scenarios can benefit from alternate uncertain ASR hypotheses, as observed in our recent work.
Identification of Multiword Expressions in Tweets for Hate Speech Detection
In this article, we present joint experiments on these two related tasks on English Twitter data: first we focus on the MWE identification task, and then we observe the influence of MWE-based features on the HSD task.
Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification
In this paper, label propagation-based semi-supervised learning is explored for the task of hate speech classification.
SAMbA: Speech enhancement with Asynchronous ad-hoc Microphone Arrays
Speech enhancement in ad-hoc microphone arrays is often hindered by the asynchronization of the devices composing the microphone array.
Transferring Knowledge via Neighborhood-Aware Optimal Transport for Low-Resource Hate Speech Detection
The concerning rise of hateful content on online platforms has increased the attention towards automatic hate speech detection, commonly formulated as a supervised classification task.
Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection
State-of-the-art approaches for hate-speech detection usually exhibit poor performance in out-of-domain settings.
Placing M-Phasis on the Plurality of Hate: A Feature-Based Corpus of Hate Online
Even though hate speech (HS) online has been an important object of research in the last decade, most HS-related corpora over-simplify the phenomenon of hate by attempting to label user comments as "hate" or "neutral".
Dynamically Refined Regularization for Improving Cross-corpora Hate Speech Detection
In this paper, we propose to automatically identify and reduce spurious correlations using attribution methods with dynamic refinement of the list of terms that need to be regularized during training.
Attention-based distributed speech enhancement for unconstrained microphone arrays with varying number of nodes
Speech enhancement promises higher efficiency in ad-hoc microphone arrays than in constrained microphone arrays thanks to the wide spatial coverage of the devices in the acoustic scene.
Improving Automatic Hate Speech Detection with Multiword Expression Features
To incorporate MWE features, we create a three-branch deep neural network: one branch for USE, one for MWE categories, and one for MWE embeddings.
DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays
Deep neural network (DNN)-based speech enhancement algorithms in microphone arrays have now proven to be efficient solutions to speech understanding and speech recognition in noisy environments.
Distributed speech separation in spatially unconstrained microphone arrays
We propose a distributed algorithm that can process spatial information in a spatially unconstrained microphone array.
DNN-Based Semantic Model for Rescoring N-best Speech Recognition List
We propose to perform this through rescoring of the ASR N-best hypotheses list.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Adaptation de domaine non supervis\'ee pour la reconnaissance de la langue par r\'egularisation d'un r\'eseau de neurones (Unsupervised domain adaptation for language identification by regularization of a neural network)
Sur le corpus RATS, pour sept des huit canaux radio {\'e}tudi{\'e}s, l{'}approche permet, sans utiliser de donn{\'e}es annot{\'e}es du domaine cible, de surpasser la performance d{'}un syst{\`e}me entra{\^\i}n{\'e} de fa{\c{c}}on supervis{\'e}e avec des donn{\'e}es annot{\'e}es de ce domaine.
Introduction d'informations s\'emantiques dans un syst\`eme de reconnaissance de la parole (Despite spectacular advances in recent years, the Automatic Speech Recognition (ASR) systems still make mistakes, especially in noisy environments)
Malgr{\'e} les avanc{\'e}s spectaculaires ces derni{\`e}res ann{\'e}es, les syst{\`e}mes de Reconnaissance Automatique de Parole (RAP) commettent encore des erreurs, surtout dans des environnements bruit{\'e}s. Pour am{\'e}liorer la RAP, nous proposons de se diriger vers une contextualisation d{'}un syst{\`e}me RAP, car les informations s{\'e}mantiques sont importantes pour la performance de la RAP.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Reconnaissance automatique de la parole : g\'en\'eration des prononciations non natives pour l'enrichissement du lexique (In this study we propose a method for lexicon adaptation in order to improve the automatic speech recognition (ASR) of non-native speakers)
Pour prendre en compte ce probl{\`e}me de prononciations erron{\'e}es, notre approche propose d{'}int{\'e}grer les prononciations non natives dans le lexique et par la suite d{'}utiliser ce lexique enrichi pour la reconnaissance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
DNN-Based Distributed Multichannel Mask Estimation for Speech Enhancement in Microphone Arrays
Multichannel processing is widely used for speech enhancement but several limitations appear when trying to deploy these solutions to the real-world.
Towards non-toxic landscapes: Automatic toxic comment detection using DNN
The contribution of this paper is the design of binary classification and regression-based approaches aiming to predict whether a comment is toxic or not.
How Diachronic Text Corpora Affect Context based Retrieval of OOV Proper Names for Audio News
Out-Of-Vocabulary (OOV) words missed by Large Vocabulary Continuous Speech Recognition (LVCSR) systems can be recovered with the help of topic and semantic context of the OOV words captured from a diachronic text corpus.
Learning to retrieve out-of-vocabulary words in speech recognition
In this paper, we propose two neural network models targeted to retrieve OOV PNs relevant to an audio document: (a) Document level Continuous Bag of Words (D-CBOW), (b) Document level Continuous Bag of Weighted Words (D-CBOW2).
