Papers by Damien Lolive

9 papers
SocialForge: simulating the social internet to provide realistic training against influence operations (2025.acl-industry)

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Challenge: Social media platforms have enabled large-scale influence campaigns, impacting democratic processes.
Approach: They propose a system to enhance diversity and realism of the generated content while ensuring its adherence to the original scenario.
Outcome: The proposed system improves diversity and realism while ensuring its adherence to the original scenario.
Investigating Inter- and Intra-speaker Voice Conversion using Audiobooks (2022.lrec-1)

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Challenge: Audiobook readers play with their voices to emphasize some text passages, highlight discourse changes or significant events, or in order to make listening easier and entertaining.
Approach: They propose to modify the narrator’s voice to fit the context of the story, such as the character who is speaking, using voice conversion.
Outcome: The proposed method improves the quality of the voice conversion system and the speaker similarity.
Style versus Content: A distinction without a (learnable) difference? (2020.coling-main)

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Challenge: Textual style transfer assumes that it is possible to separate style from content . however, style transfer can provide insight into language more generally .
Approach: They propose to use sentiment transfer to examine whether style transfer is possible . they employ adversarial encoder-decoder networks to analyze style-related features .
Outcome: The proposed method combines style transfer with content preservation and fluency to show that style cannot be usefully separated from content within style transfer systems.
Paraphrase Generation Evaluation Powered by an LLM: A Semantic Metric, Not a Lexical One (2025.coling-main)

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Challenge: Existing measures for automatic paraphrase generation are based on lexical distances or semantic embedding alignments.
Approach: They propose a measure based on a log likelihood ratio from an LLM to assess the quality of a potential paraphrase.
Outcome: The proposed measure is better for sorting pairs of sentences by semantic proximity and provides an interpretable classification threshold between paraphrases and non-paraphrases.
EMO&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context. (L18-1)

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Challenge: Anomalies in discourse are induced or acted by a machine learning algorithm.
Approach: They propose to use facial and speech video to create a corpus that contains controlled anomalies.
Outcome: The proposed corpus contains controlled anomalies in speech and facial video recordings of subjects.
SynPaFlex-Corpus: An Expressive French Audiobooks Corpus dedicated to expressive speech synthesis. (L18-1)

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Challenge: a French audiobooks corpus contains 87 hours of good audio quality speech . audiobooks provide mono-genre and multi-speaker speech whereas audiobooks usually provide a few hours of mono- and multispeakers .
Approach: They present an expressive French audiobooks corpus containing eighty seven hours of speech . the corpus is annotated automatically and provides information as phone labels, phone boundaries, syllables, words or morpho-syntactic tagging.
Outcome: The proposed corpus contains 87 hours of speech recorded by a single speaker . the data will allow developing models to better control expressiveness in speech synthesis .
A Low-Cost Motion Capture Corpus in French Sign Language for Interpreting Iconicity and Spatial Referencing Mechanisms (2022.lrec-1)

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Challenge: Existing tools for automatic translation of sign language videos into transcribed texts are limited.
Approach: They propose to use deep learning methods to circumvent the use of models in spatial referencing recognition by a 3D skeleton and a software program to capture and post-process the LSF-SHELVES corpus.
Outcome: The proposed system targets iconicity and spatial referencing in french sign language . it is light-weight and low-cost to collect data from a large panel of signers .
When depth is redundant: Efficient transformer-based speech anti-spoofing (2026.findings-acl)

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Challenge: Existing anti-spoofing countermeasures exhibit limited generalization to unseen spoof attacks, especially in out-of-domain evaluation settings.
Approach: They propose a training strategy that aligns shallow and intermediate representations with those of the final transformer layer for speech deepfake detection.
Outcome: The proposed model improves robustness to unseen spoofing attacks and enhances out-of-domain generalization over strong baselines.
Neural-Driven Search-Based Paraphrase Generation (2021.eacl-main)

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Challenge: Existing non-supervised paraphrase generation models are biased toward specific problems like question answering or image captioning.
Approach: They propose a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical distance.
Outcome: The proposed algorithms perform well against non-supervised baselines.

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