Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Moradi, A.; Falk, T. H.
Benchmarking Foundation Models for Cross-Domain Speaker Profiling Proceedings Article
In: IEEE Conf. Artif. Intell., CAI, pp. 78–84, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 979-833156039-3 (ISBN), (Journal Abbreviation: IEEE Conf. Artif. Intell., CAI).
Abstract | Links | BibTeX | Tags: Benchmarking, Continuous speech recognition, Cross-domain, Forecasting, Formant frequency, Foundation models, Learning systems, Linguistics, Multi-attributes, Multi-task learning, Paralinguistic, Performance, Speaker identification, Speaker verification, Speech communication, State of the art, Verification task
@inproceedings{moradiBenchmarkingFoundationModels2026,
title = {Benchmarking Foundation Models for Cross-Domain Speaker Profiling},
author = {A. Moradi and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105042046048?origin=resultslist},
doi = {10.1109/CAI68641.2026.11536565},
isbn = {979-833156039-3 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {IEEE Conf. Artif. Intell., CAI},
pages = {78–84},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Speech conveys both linguistic and paralinguistic content. While pre-trained speech foundation models have been widely explored for linguistic tasks, such as speech recognition, and for speaker identification and verification tasks, very limited work has been done to test their usefulness for multi-attribute speaker profiling, i.e., simultaneous prediction of biological sex, age, and height from speech. In this paper, we aim to benchmark the performance of four state-of-the-art self-supervised foundation models, namely, WavLM, Wav2vec2, HuBERT, and XLSR-53, under both within- and cross-domain conditions. Each model is employed as a frozen pre-trained feature extractor, with lightweight task-specific heads trained jointly in a multi-task learning framework, and their feature extraction latency is empirically analyzed to assess practical deployment considerations. Experiments on two datasets show that WavLM achieves consistently strong within- and cross-domain performance for biological sex prediction, while Wav2vec2 and XLSR-53 exhibit more consistent performance for the age and height regression tasks in cross-domain settings. Interpretability analysis based on correlations with key acoustic features show (1) WavLM internal representations correlating highly with pitch and formant frequencies, corroborating the improved performance on biological sex prediction, and (2) XLSR-53 correlating highly with the second formant frequency, corroborating the results obtained for age and height. Overall, our analysis shows that pre-trained speech foundation models could serve as useful tools for cross-domain speaker profiling tasks. While no model stood out as a clear winner across all tested physical traits, future work could explore the use of ensemble methods for improved generalizability. © 2026 IEEE.},
note = {Journal Abbreviation: IEEE Conf. Artif. Intell., CAI},
keywords = {Benchmarking, Continuous speech recognition, Cross-domain, Forecasting, Formant frequency, Foundation models, Learning systems, Linguistics, Multi-attributes, Multi-task learning, Paralinguistic, Performance, Speaker identification, Speaker verification, Speech communication, State of the art, Verification task},
pubstate = {published},
tppubtype = {inproceedings}
}
Speech conveys both linguistic and paralinguistic content. While pre-trained speech foundation models have been widely explored for linguistic tasks, such as speech recognition, and for speaker identification and verification tasks, very limited work has been done to test their usefulness for multi-attribute speaker profiling, i.e., simultaneous prediction of biological sex, age, and height from speech. In this paper, we aim to benchmark the performance of four state-of-the-art self-supervised foundation models, namely, WavLM, Wav2vec2, HuBERT, and XLSR-53, under both within- and cross-domain conditions. Each model is employed as a frozen pre-trained feature extractor, with lightweight task-specific heads trained jointly in a multi-task learning framework, and their feature extraction latency is empirically analyzed to assess practical deployment considerations. Experiments on two datasets show that WavLM achieves consistently strong within- and cross-domain performance for biological sex prediction, while Wav2vec2 and XLSR-53 exhibit more consistent performance for the age and height regression tasks in cross-domain settings. Interpretability analysis based on correlations with key acoustic features show (1) WavLM internal representations correlating highly with pitch and formant frequencies, corroborating the improved performance on biological sex prediction, and (2) XLSR-53 correlating highly with the second formant frequency, corroborating the results obtained for age and height. Overall, our analysis shows that pre-trained speech foundation models could serve as useful tools for cross-domain speaker profiling tasks. While no model stood out as a clear winner across all tested physical traits, future work could explore the use of ensemble methods for improved generalizability. © 2026 IEEE.
2.
Pimentel, A.; Zhu, Y.; Falk, T. H.
Partial Audio Deepfake Detection: Are We Really Detecting Synthetic Media or Just Dataset Content Biases? Proceedings Article
In: IEEE Conf. Artif. Intell., CAI, pp. 1846–1851, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 979-833156039-3 (ISBN), (Journal Abbreviation: IEEE Conf. Artif. Intell., CAI).
Abstract | Links | BibTeX | Tags: Audio signal processing, Computational linguistics, Condition, Detection models, Information integrity, Language model, Large datasets, Learning models, Optimistics, Performance, Self-supervised learning, Speech communication, Speech models, Speech recognition, Speech signals, Synthetic media, Transcription
@inproceedings{pimentelPartialAudioDeepfake2026,
title = {Partial Audio Deepfake Detection: Are We Really Detecting Synthetic Media or Just Dataset Content Biases?},
author = {A. Pimentel and Y. Zhu and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105042045476?origin=resultslist},
doi = {10.1109/CAI68641.2026.11536622},
isbn = {979-833156039-3 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {IEEE Conf. Artif. Intell., CAI},
pages = {1846–1851},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In recent years, the generation of highly realistic audio deepfakes has raised significant concerns regarding privacy and information integrity. While most research has focused on fully bonafide or spoofed speech, partial deep-fakes, where only segments of an utterance are manipulated, remain less explored. Given the nature of the task, existing datasets are relying on large language models to manipulate bonafide speech signals into partial deepfakes by altering, deleting, or replacing segments with synthetic content. These manipulations may alter the semantics and sentiment of the generated content, creating biases that can be captured by deepfake detection models, leading to overly optimistic performance and poor generalizability. In this work, we investigate the presence of such biases in two popular partial deepfake datasets, namely AV-Deepfake1M and PartialEdit. We explore four self-supervised learning models, two relying only on text transcriptions (RoBERTa and RoBERTa-sentiment) and two relying on universal speech representations (WavLM Large and Wav2vec2-XLSR). Our experiments show that, while speech models achieve higher in-domain performance, they do not generalize to out-of-domain conditions. In turn, models trained on only transcribed speech can effectively distinguish manipulated content, achieving up to 97.8% AUC in-domain and nearly 70% AUC out-of-domain. These results suggest that linguistic patterns and dataset confounds may indeed be biasing partial deepfake detection models, leading to poor generalizability. © 2026 IEEE.},
note = {Journal Abbreviation: IEEE Conf. Artif. Intell., CAI},
keywords = {Audio signal processing, Computational linguistics, Condition, Detection models, Information integrity, Language model, Large datasets, Learning models, Optimistics, Performance, Self-supervised learning, Speech communication, Speech models, Speech recognition, Speech signals, Synthetic media, Transcription},
pubstate = {published},
tppubtype = {inproceedings}
}
In recent years, the generation of highly realistic audio deepfakes has raised significant concerns regarding privacy and information integrity. While most research has focused on fully bonafide or spoofed speech, partial deep-fakes, where only segments of an utterance are manipulated, remain less explored. Given the nature of the task, existing datasets are relying on large language models to manipulate bonafide speech signals into partial deepfakes by altering, deleting, or replacing segments with synthetic content. These manipulations may alter the semantics and sentiment of the generated content, creating biases that can be captured by deepfake detection models, leading to overly optimistic performance and poor generalizability. In this work, we investigate the presence of such biases in two popular partial deepfake datasets, namely AV-Deepfake1M and PartialEdit. We explore four self-supervised learning models, two relying only on text transcriptions (RoBERTa and RoBERTa-sentiment) and two relying on universal speech representations (WavLM Large and Wav2vec2-XLSR). Our experiments show that, while speech models achieve higher in-domain performance, they do not generalize to out-of-domain conditions. In turn, models trained on only transcribed speech can effectively distinguish manipulated content, achieving up to 97.8% AUC in-domain and nearly 70% AUC out-of-domain. These results suggest that linguistic patterns and dataset confounds may indeed be biasing partial deepfake detection models, leading to poor generalizability. © 2026 IEEE.
3.
Ngouanfouo, C.; Davoust, A.
Detecting Machine-Generated Text using Grammatical Features Proceedings Article
In: Proc. Int. Conf. Tools Artif. Intell. ICTAI, pp. 843–848, IEEE Computer Society, 2025, ISBN: 10823409 (ISSN); 979-833154919-0 (ISBN).
Abstract | Links | BibTeX | Tags: AI Text Detection, CNN, Computational grammars, Detection methods, Language model, Machine-generated texts, Natural language generation, Natural language processing systems, Neural encoding, Neural modelling, Part Of Speech, Part-of Speech, Speech communication, Text detection, Written texts
@inproceedings{ngouanfouo_detecting_2025,
title = {Detecting Machine-Generated Text using Grammatical Features},
author = {C. Ngouanfouo and A. Davoust},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105031903675&doi=10.1109%2FICTAI66417.2025.00123&partnerID=40&md5=5783b8797a3425f9dfa737343ee757d2},
doi = {10.1109/ICTAI66417.2025.00123},
isbn = {10823409 (ISSN); 979-833154919-0 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. Int. Conf. Tools Artif. Intell. ICTAI},
pages = {843–848},
publisher = {IEEE Computer Society},
abstract = {Large Language Models (LLMs) have advanced natural language generation but pose ethical and practical challenges, making it crucial to detect machine-generated texts. Traditional detection methods rely on complex, hard-to-interpret neural encodings and model-specific features like perplexity. This study explores whether grammatical patterns-specifically sequences of parts of speech (POS), including punctuation and symbols-can distinguish machine-written texts from human ones. Using a CNN classifier on POS sequences, the approach achieves nearly 90 % accuracy on a benchmark dataset. Combining POS-based features with neural embeddings improves performance, and the model shows robustness against adversarial attacks, though it is less effective on short texts. © 2025 IEEE.},
keywords = {AI Text Detection, CNN, Computational grammars, Detection methods, Language model, Machine-generated texts, Natural language generation, Natural language processing systems, Neural encoding, Neural modelling, Part Of Speech, Part-of Speech, Speech communication, Text detection, Written texts},
pubstate = {published},
tppubtype = {inproceedings}
}
Large Language Models (LLMs) have advanced natural language generation but pose ethical and practical challenges, making it crucial to detect machine-generated texts. Traditional detection methods rely on complex, hard-to-interpret neural encodings and model-specific features like perplexity. This study explores whether grammatical patterns-specifically sequences of parts of speech (POS), including punctuation and symbols-can distinguish machine-written texts from human ones. Using a CNN classifier on POS sequences, the approach achieves nearly 90 % accuracy on a benchmark dataset. Combining POS-based features with neural embeddings improves performance, and the model shows robustness against adversarial attacks, though it is less effective on short texts. © 2025 IEEE.
4.
Temmar, D. E.; Hamadene, A.; Nallaguntla, V.; Fursule, A.; Allili, M. S.; Kshirsagar, S.; Avila, A. R.
Phonetic Analysis of Real and Synthetic Speech Using HuBERT Embeddings: Perspectives for Deepfake Detection Proceedings Article
In: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern., pp. 86–91, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 1062922X (ISSN); 979-833153358-8 (ISBN), (Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Audio acoustics, Audio DeepFake Detection, Audio signal processing, Embeddings, Hu-BERT, KL-divergence, Linguistics, Phoneme and word Embedding, Phonetic analysis, Security systems, Self-Supervised Speech Representation, Speech analysis, Speech communication, Speech processing, Speech synthesis, Synthetic speech, Text to speech, Voice conversion
@inproceedings{temmarPhoneticAnalysisReal2025,
title = {Phonetic Analysis of Real and Synthetic Speech Using HuBERT Embeddings: Perspectives for Deepfake Detection},
author = {D. E. Temmar and A. Hamadene and V. Nallaguntla and A. Fursule and M. S. Allili and S. Kshirsagar and A. R. Avila},
url = {https://www.scopus.com/pages/publications/105033145913?origin=resultslist},
doi = {10.1109/SMC58881.2025.11343334},
isbn = {1062922X (ISSN); 979-833153358-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
pages = {86–91},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The growing sophistication of speech generated by Artificial Intelligence (AI) has introduced new challenges in audio deepfake detection. Text-to-speech (TTS) and voice conversion (VC) technologies can now produce convincing synthetic speech with high quality and intelligibility. This poses a serious threat to voice biometric security systems, such as automatic speaker recognition. It also increases the risks associated to the spread of spoken disinformation, where synthetic voices can be used to disseminate malicious content. In this study, we conduct an analysis of real and synthetic speech at phonetic and word levels. For that, a parallel dataset comprising real and synthetic speech signals were developed based on a subset of the LibriSpeech ASR corpus. Synthetic speech samples were generated using two TTS and one VC systems: Coqui TTS, VITS TTS, and StarGANv2 VC. We adopted HuBERT, a self-supervised speech model, to extract speech embeddings. The motivation for using this model stems from its ability to recognize sound units corresponding to the so-called pseudo phonemes. Our analysis is based on the KL divergence (KLD) between the distributions of synthetic and real phonemes, which allowed us to rank synthetic phonemes based on their alignment with their real counterpart. We also trained several classifiers per phoneme to distinguish between real and synthetic samples. We then compute the correlations between KLD and accuracies per phoneme. Besides showing a list of phonemes that are more discriminative, our findings suggest that vowels correlate better with the classifiers' performance, suggesting that the KLD can be an indicator of the most distinguishable phonemes for deepfake detection. © 2025 IEEE.},
note = {Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
keywords = {Artificial intelligence, Audio acoustics, Audio DeepFake Detection, Audio signal processing, Embeddings, Hu-BERT, KL-divergence, Linguistics, Phoneme and word Embedding, Phonetic analysis, Security systems, Self-Supervised Speech Representation, Speech analysis, Speech communication, Speech processing, Speech synthesis, Synthetic speech, Text to speech, Voice conversion},
pubstate = {published},
tppubtype = {inproceedings}
}
The growing sophistication of speech generated by Artificial Intelligence (AI) has introduced new challenges in audio deepfake detection. Text-to-speech (TTS) and voice conversion (VC) technologies can now produce convincing synthetic speech with high quality and intelligibility. This poses a serious threat to voice biometric security systems, such as automatic speaker recognition. It also increases the risks associated to the spread of spoken disinformation, where synthetic voices can be used to disseminate malicious content. In this study, we conduct an analysis of real and synthetic speech at phonetic and word levels. For that, a parallel dataset comprising real and synthetic speech signals were developed based on a subset of the LibriSpeech ASR corpus. Synthetic speech samples were generated using two TTS and one VC systems: Coqui TTS, VITS TTS, and StarGANv2 VC. We adopted HuBERT, a self-supervised speech model, to extract speech embeddings. The motivation for using this model stems from its ability to recognize sound units corresponding to the so-called pseudo phonemes. Our analysis is based on the KL divergence (KLD) between the distributions of synthetic and real phonemes, which allowed us to rank synthetic phonemes based on their alignment with their real counterpart. We also trained several classifiers per phoneme to distinguish between real and synthetic samples. We then compute the correlations between KLD and accuracies per phoneme. Besides showing a list of phonemes that are more discriminative, our findings suggest that vowels correlate better with the classifiers' performance, suggesting that the KLD can be an indicator of the most distinguishable phonemes for deepfake detection. © 2025 IEEE.
5.
Ngouanfouo, C.; Davoust, A.
Detecting Machine-Generated Text using Grammatical Features Proceedings Article
In: Proc. Int. Conf. Tools Artif. Intell. ICTAI, pp. 843–848, IEEE Computer Society, 2025, ISBN: 10823409 (ISSN); 979-833154919-0 (ISBN), (Journal Abbreviation: Proc. Int. Conf. Tools Artif. Intell. ICTAI).
Abstract | Links | BibTeX | Tags: AI Text Detection, CNN, Computational grammars, Detection methods, Language model, Machine-generated texts, Natural language generation, Natural language processing systems, Neural encoding, Neural modelling, Part Of Speech, Part-of Speech, Speech communication, Text detection, Written texts
@inproceedings{ngouanfouoDetectingMachineGeneratedText2025,
title = {Detecting Machine-Generated Text using Grammatical Features},
author = {C. Ngouanfouo and A. Davoust},
url = {https://www.scopus.com/pages/publications/105031903675?origin=resultslist},
doi = {10.1109/ICTAI66417.2025.00123},
isbn = {10823409 (ISSN); 979-833154919-0 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. Int. Conf. Tools Artif. Intell. ICTAI},
pages = {843–848},
publisher = {IEEE Computer Society},
abstract = {Large Language Models (LLMs) have advanced natural language generation but pose ethical and practical challenges, making it crucial to detect machine-generated texts. Traditional detection methods rely on complex, hard-to-interpret neural encodings and model-specific features like perplexity. This study explores whether grammatical patterns-specifically sequences of parts of speech (POS), including punctuation and symbols-can distinguish machine-written texts from human ones. Using a CNN classifier on POS sequences, the approach achieves nearly 90 % accuracy on a benchmark dataset. Combining POS-based features with neural embeddings improves performance, and the model shows robustness against adversarial attacks, though it is less effective on short texts. © 2025 IEEE.},
note = {Journal Abbreviation: Proc. Int. Conf. Tools Artif. Intell. ICTAI},
keywords = {AI Text Detection, CNN, Computational grammars, Detection methods, Language model, Machine-generated texts, Natural language generation, Natural language processing systems, Neural encoding, Neural modelling, Part Of Speech, Part-of Speech, Speech communication, Text detection, Written texts},
pubstate = {published},
tppubtype = {inproceedings}
}
Large Language Models (LLMs) have advanced natural language generation but pose ethical and practical challenges, making it crucial to detect machine-generated texts. Traditional detection methods rely on complex, hard-to-interpret neural encodings and model-specific features like perplexity. This study explores whether grammatical patterns-specifically sequences of parts of speech (POS), including punctuation and symbols-can distinguish machine-written texts from human ones. Using a CNN classifier on POS sequences, the approach achieves nearly 90 % accuracy on a benchmark dataset. Combining POS-based features with neural embeddings improves performance, and the model shows robustness against adversarial attacks, though it is less effective on short texts. © 2025 IEEE.
6.
Zhu, Y.; Davoust, A.; Falk, T. H.
DeepSick: Deceiving Voice-Based Diagnostic Models with Synthetic Multilingual Pathological Speech Signals Proceedings Article
In: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern., pp. 69–74, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 1062922X (ISSN); 979-833153358-8 (ISBN), (Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.).
Abstract | Links | BibTeX | Tags: COVID-19, Detection models, Diagnosis, Diagnostic model, Diagnostic systems, Generative model, Health assessments, Pathological conditions, Pathological speech signals, Scalable solution, Speech communication, Speech recognition, Speech synthesis, State of the art, Voice model
@inproceedings{zhuDeepSickDeceivingVoiceBased2025,
title = {DeepSick: Deceiving Voice-Based Diagnostic Models with Synthetic Multilingual Pathological Speech Signals},
author = {Y. Zhu and A. Davoust and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105033143787?origin=resultslist},
doi = {10.1109/SMC58881.2025.11343240},
isbn = {1062922X (ISSN); 979-833153358-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
pages = {69–74},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Voice-based diagnostic systems offer a scalable solution for remote health assessment. However, recent advances in generative voice models may enable malicious manipulation of voice samples to simulate or conceal disease-related speech characteristics, which poses new risks to diagnostic systems. This paper investigates the vulnerability of diagnostic and detection models to such types of "deepfake"attacks. We show that it is possible to train a generative model to convert between healthy voices and pathological ones, which in turn, can successfully deceive existing diagnostic systems. Here, focus is placed on COVID-19 infection and respiratory abnormalities, but the method can be applied across different pathological conditions affecting vocal attributes. We also benchmark four state-of-the-art synthesized voice detection models on both real and generated pathological speech from three datasets. Our results show that current synthetic voice detectors, typically trained on healthy speech data, perform poorly on generated pathological samples. While fine-tuning with real pathological voices improves detection, a substantial performance gap remains. This work provides initial insights on an emerging threat to remote voice diagnostic systems that needs further work. © 2025 IEEE.},
note = {Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
keywords = {COVID-19, Detection models, Diagnosis, Diagnostic model, Diagnostic systems, Generative model, Health assessments, Pathological conditions, Pathological speech signals, Scalable solution, Speech communication, Speech recognition, Speech synthesis, State of the art, Voice model},
pubstate = {published},
tppubtype = {inproceedings}
}
Voice-based diagnostic systems offer a scalable solution for remote health assessment. However, recent advances in generative voice models may enable malicious manipulation of voice samples to simulate or conceal disease-related speech characteristics, which poses new risks to diagnostic systems. This paper investigates the vulnerability of diagnostic and detection models to such types of "deepfake"attacks. We show that it is possible to train a generative model to convert between healthy voices and pathological ones, which in turn, can successfully deceive existing diagnostic systems. Here, focus is placed on COVID-19 infection and respiratory abnormalities, but the method can be applied across different pathological conditions affecting vocal attributes. We also benchmark four state-of-the-art synthesized voice detection models on both real and generated pathological speech from three datasets. Our results show that current synthetic voice detectors, typically trained on healthy speech data, perform poorly on generated pathological samples. While fine-tuning with real pathological voices improves detection, a substantial performance gap remains. This work provides initial insights on an emerging threat to remote voice diagnostic systems that needs further work. © 2025 IEEE.
7.
Ngouanfouo, C.; Davoust, A.
Detecting Machine-Generated Text using Grammatical Features Proceedings Article
In: Proc. Int. Conf. Tools Artif. Intell. ICTAI, pp. 843–848, IEEE Computer Society, 2025, ISBN: 10823409 (ISSN); 979-833154919-0 (ISBN).
Abstract | Links | BibTeX | Tags: AI Text Detection, CNN, Computational grammars, Detection methods, Language model, Machine-generated texts, Natural language generation, Natural language processing systems, Neural encoding, Neural modelling, Part Of Speech, Part-of Speech, Speech communication, Text detection, Written texts
@inproceedings{ngouanfouoDetectingMachineGeneratedText2025a,
title = {Detecting Machine-Generated Text using Grammatical Features},
author = {C. Ngouanfouo and A. Davoust},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105031903675&doi=10.1109%2FICTAI66417.2025.00123&partnerID=40&md5=5783b8797a3425f9dfa737343ee757d2},
doi = {10.1109/ICTAI66417.2025.00123},
isbn = {10823409 (ISSN); 979-833154919-0 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. Int. Conf. Tools Artif. Intell. ICTAI},
pages = {843–848},
publisher = {IEEE Computer Society},
abstract = {Large Language Models (LLMs) have advanced natural language generation but pose ethical and practical challenges, making it crucial to detect machine-generated texts. Traditional detection methods rely on complex, hard-to-interpret neural encodings and model-specific features like perplexity. This study explores whether grammatical patterns-specifically sequences of parts of speech (POS), including punctuation and symbols-can distinguish machine-written texts from human ones. Using a CNN classifier on POS sequences, the approach achieves nearly 90 % accuracy on a benchmark dataset. Combining POS-based features with neural embeddings improves performance, and the model shows robustness against adversarial attacks, though it is less effective on short texts. © 2025 IEEE.},
keywords = {AI Text Detection, CNN, Computational grammars, Detection methods, Language model, Machine-generated texts, Natural language generation, Natural language processing systems, Neural encoding, Neural modelling, Part Of Speech, Part-of Speech, Speech communication, Text detection, Written texts},
pubstate = {published},
tppubtype = {inproceedings}
}
Large Language Models (LLMs) have advanced natural language generation but pose ethical and practical challenges, making it crucial to detect machine-generated texts. Traditional detection methods rely on complex, hard-to-interpret neural encodings and model-specific features like perplexity. This study explores whether grammatical patterns-specifically sequences of parts of speech (POS), including punctuation and symbols-can distinguish machine-written texts from human ones. Using a CNN classifier on POS sequences, the approach achieves nearly 90 % accuracy on a benchmark dataset. Combining POS-based features with neural embeddings improves performance, and the model shows robustness against adversarial attacks, though it is less effective on short texts. © 2025 IEEE.
8.
Remacle, A.; Bouchard, S.; Morsomme, D.
In: Computers and Education, vol. 200, 2023, ISSN: 03601315 (ISSN), (Publisher: Elsevier Ltd).
Abstract | Links | BibTeX | Tags: Background noise, Computer aided instruction, Control groups, E-learning, Experimental groups, Oral communication, Oral communication skills, Personnel training, randomized controlled trial, Self Efficacy, Speech communication, Teacher training, Teachers', Virtual Classroom, virtual reality, Voice
@article{remacle_can_2023,
title = {Can teaching simulations in a virtual classroom help trainee teachers to develop oral communication skills and self-efficacy? A randomized controlled trial.},
author = {A. Remacle and S. Bouchard and D. Morsomme},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152896047&doi=10.1016%2fj.compedu.2023.104808&partnerID=40&md5=990f5fff277e3edf28e78b1fab80022c},
doi = {10.1016/j.compedu.2023.104808},
issn = {03601315 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {Computers and Education},
volume = {200},
publisher = {Elsevier Ltd},
abstract = {Effective oral communication skills are essential to ensure optimal teaching while preserving the teacher's vocal health. Training these skills in representative settings is expected to promote their generalization. Since the implementation of such training in actual school situations is challenging, virtual reality (VR) may represent a solution.This study evaluated the effects of VR simulations on trainee teachers’ oral communication skills. Based on our Theoretical Framework for Teachers’ Vocal Behavior, we developed and empirically assessed a voice-related prevention program including noisy communicative situations in a virtual classroom. In a randomized controlled trial, the participants were assigned to one of two conditions: (1) individual voice training including simulations in the virtual classroom and a group information session (experimental group},
note = {Publisher: Elsevier Ltd},
keywords = {Background noise, Computer aided instruction, Control groups, E-learning, Experimental groups, Oral communication, Oral communication skills, Personnel training, randomized controlled trial, Self Efficacy, Speech communication, Teacher training, Teachers', Virtual Classroom, virtual reality, Voice},
pubstate = {published},
tppubtype = {article}
}
Effective oral communication skills are essential to ensure optimal teaching while preserving the teacher's vocal health. Training these skills in representative settings is expected to promote their generalization. Since the implementation of such training in actual school situations is challenging, virtual reality (VR) may represent a solution.This study evaluated the effects of VR simulations on trainee teachers’ oral communication skills. Based on our Theoretical Framework for Teachers’ Vocal Behavior, we developed and empirically assessed a voice-related prevention program including noisy communicative situations in a virtual classroom. In a randomized controlled trial, the participants were assigned to one of two conditions: (1) individual voice training including simulations in the virtual classroom and a group information session (experimental group
9.
Remacle, A.; Bouchard, S.; Morsomme, D.
In: Computers and Education, vol. 200, 2023, ISSN: 03601315 (ISSN).
Abstract | Links | BibTeX | Tags: Background noise, Computer aided instruction, Control groups, E-learning, Experimental groups, Oral communication, Oral communication skills, Personnel training, randomized controlled trial, Self Efficacy, Speech communication, Teacher training, Teachers', Virtual Classroom, virtual reality, Voice
@article{remacleCanTeachingSimulations2023,
title = {Can teaching simulations in a virtual classroom help trainee teachers to develop oral communication skills and self-efficacy? A randomized controlled trial.},
author = {A. Remacle and S. Bouchard and D. Morsomme},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152896047&doi=10.1016%2fj.compedu.2023.104808&partnerID=40&md5=990f5fff277e3edf28e78b1fab80022c},
doi = {10.1016/j.compedu.2023.104808},
issn = {03601315 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {Computers and Education},
volume = {200},
publisher = {Elsevier Ltd},
abstract = {Effective oral communication skills are essential to ensure optimal teaching while preserving the teacher's vocal health. Training these skills in representative settings is expected to promote their generalization. Since the implementation of such training in actual school situations is challenging, virtual reality (VR) may represent a solution.This study evaluated the effects of VR simulations on trainee teachers’ oral communication skills. Based on our Theoretical Framework for Teachers’ Vocal Behavior, we developed and empirically assessed a voice-related prevention program including noisy communicative situations in a virtual classroom. In a randomized controlled trial, the participants were assigned to one of two conditions: (1) individual voice training including simulations in the virtual classroom and a group information session (experimental group},
keywords = {Background noise, Computer aided instruction, Control groups, E-learning, Experimental groups, Oral communication, Oral communication skills, Personnel training, randomized controlled trial, Self Efficacy, Speech communication, Teacher training, Teachers', Virtual Classroom, virtual reality, Voice},
pubstate = {published},
tppubtype = {article}
}
Effective oral communication skills are essential to ensure optimal teaching while preserving the teacher's vocal health. Training these skills in representative settings is expected to promote their generalization. Since the implementation of such training in actual school situations is challenging, virtual reality (VR) may represent a solution.This study evaluated the effects of VR simulations on trainee teachers’ oral communication skills. Based on our Theoretical Framework for Teachers’ Vocal Behavior, we developed and empirically assessed a voice-related prevention program including noisy communicative situations in a virtual classroom. In a randomized controlled trial, the participants were assigned to one of two conditions: (1) individual voice training including simulations in the virtual classroom and a group information session (experimental group