HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis
Open accessAbstract
Authorship Analysis, also known as stylometry, has been an essential aspect of Natural Language Processing (NLP) for a long time. Likewise, the recent advancement of Large Language Models (LLMs) has made authorship analysis increasingly crucial for distinguishing between human-written and AI-generated texts. However, these authorship analysis tasks have primarily been focused on written texts, not considering spoken texts. Thus, we introduce the largest benchmark for spoken texts - HANSEN (Human ANd ai Spoken tExt beNchmark). HANSEN encompasses meticulous curation of existing speech datasets accompanied by transcripts, alongside the creation of novel AI-generated spoken text datasets. Together, it comprises 17 human datasets, and AI-generated spoken texts created using 3 prominent LLMs: ChatGPT, PaLM2, and Vicuna13B. To evaluate and demonstrate the utility of HANSEN, we perform Authorship Attribution (AA) & Author Verification (AV) on human-spoken datasets and conducted Human vs. AI spoken text detection using state-of-the-art (SOTA) models. While SOTA methods, such as, character ngram or Transformer-based model, exhibit similar AA & AV performance in human-spoken datasets compared to written ones, there is much room for improvement in AI-generated spoken text detection. The HANSEN benchmark is available at: https://huggingface.co/datasets/HANSEN-REPO/HANSEN.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Existing detection methods are mostly binary classifiers and cannot distinguish complex LLM-generated content such as mixed human-LLM input.
“current methods often focus on binary classification, failing to address the complexities of realworld scenarios like human-LLM collaboration.”
From Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement (2025) · cites this paper as [52]
The Introduction (p. 2) says substantial research focuses on distinguishing human from AI-generated text, and Related Work (p. 3) describes detectors trained on human and AI texts. This supports the point that detection commonly addresses a human-versus-AI distinction, but the paper does not say that most methods are binary classifiers or that they fail on mixed human–LLM content or collaboration. (LLM, conf=0.79)
Experimental — accuracy not yet measuredExisting supervised classifiers frequently overfit to particular domains, perform poorly on out-of-distribution data, and lose capability on unseen datasets from other domains.
“However, these classifiers often overfit to specific domains, leading to poor performance on out-of-distribution data [12,51], and their capabilities significantly degrade when applied to unseen datasets from different domains [32].”
From Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement (2025) · cites this paper as [51]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645 · see citing works
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821 · see citing works
Longman Grammar of Spoken and Written English
Broussard, K. M., Biber, D., Johansson, S., Leech, G., Conrad, S., & Finegan, E. (2000) · TESOL Quarterly · doi:10.2307/3587792 · Cited by 8,193 · see citing works
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208 · see citing works
Unsupervised Cross-lingual Representation Learning at Scale
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.747 · Cited by 5,637 · see citing works
Variation across Speech and Writing
Biber, D. (1988) · doi:10.1017/cbo9780511621024 · Cited by 5,227 · see citing works
Pointer Sentinel Mixture ModelsPreprint
Merity, S., Xiong, C., Bradbury, J., & Socher, R. (2016) · International Conference on Learning Representations · doi:10.48550/arxiv.1609.07843 · Cited by 5,051 · see citing works
LLaMA: Open and Efficient Foundation Language Models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.13971 · Cited by 4,602 · see citing works
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works
Kincaid, J. P., Fishburne, Jr., Robert P., R., Richard L., C., & Brad, S. (1975) · doi:10.21236/ada006655 · Cited by 3,497 · see citing works
BERTScore: Evaluating Text Generation with BERT
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09675 · Cited by 2,865 · see citing works
Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., & Fidler, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.11 · Cited by 2,264 · see citing works
VoxCeleb: A Large-Scale Speaker Identification Dataset
Nagrani, A., Chung, J. S., & Zisserman, A. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-950 · Cited by 2,210 · see citing works
Linguistic Inquiry and Word Count (LIWC)
Chung, C. K., & Pennebaker, J. W. (2012) · IGI Global eBooks · doi:10.4018/978-1-60960-741-8.ch012 · Cited by 2,152 · see citing works
Text Classification Algorithms: A Survey
Kowsari, K., Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L. E., & Brown, D. E. (2019) · Inf. · doi:10.3390/info10040150 · Cited by 1,605 · see citing works
Narayan, S., Cohen, S. B., & Lapata, M. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1206 · Cited by 1,207 · see citing works
A survey of modern authorship attribution methods
Stamatatos, E. (2009) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21001 · Cited by 1,176 · see citing works
Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
Stolcke, A., Ries, K., Coccaro, N., Shriberg, E. E., Bates, R. A., Jurafsky, D. S., Taylor, P., Martin, R., Ess-Dykema, C. V., & Meteer, M. W. (2000) · Computational Linguistics · doi:10.1162/089120100561737 · Cited by 1,112 · see citing works
Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.10403 · Cited by 834 · see citing works
Corpus of Contemporary American English (COCA)
Davies, M. G. (2015) · Harvard Dataverse · doi:10.7910/dvn/amuduw · Cited by 589 · see citing works
Defending Against Neural Fake News
Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1905.12616 · Cited by 569 · see citing works
GLTR: Statistical Detection and Visualization of Generated Text
Gehrmann, S., Strobelt, H., & Rush, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/p19-3019 · Cited by 470 · see citing works
Aston, G., & Burnard, L. (2020) · Edinburgh University Press eBooks · doi:10.1515/9780748628889-004 · Cited by 436 · see citing works
Crosslingual Generalization through Multitask Finetuning
Muennighoff, N., Wang, T., Sutawika, L., Roberts, A., Biderman, S., Le Scao, T., Bari, M. S., Shen, S., Yong, Z. X., Schoelkopf, H., et al. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.891 · Cited by 361 · see citing works
Wang, C., Rivière, M., Lee, A., Wu, A., Talnikar, C., Haziza, D., Williamson, M., Pino, J., & Emmanuel Dupoux, A. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.80 · Cited by 339 · see citing works
Danescu-Niculescu-Mizil, C., & Lee, L. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1106.3077 · Cited by 329 · see citing works
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., Yue, J., & Wu, Y. (2023) · arXiv · doi:10.48550/arxiv.2301.07597 · Cited by 318 · see citing works
Release Strategies and the Social Impacts of Language Models
Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., Radford, A., Krueger, G., Kim, J. W., Kreps, S., et al. (2019) · arXiv · doi:10.48550/arxiv.1908.09203 · Cited by 292 · see citing works
All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text
Clark, E., August, T., Serrano, S., Haduong, N., Gururangan, S., & Smith, N. A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.565 · Cited by 290 · see citing works
All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text
Clark, E., August, T., Serrano, S., Haduong, N., Gururangan, S., & Smith, N. A. (2021) · arXiv · doi:10.48550/arxiv.2107.00061 · Cited by 290
Surveying Stylometry Techniques and Applications
Neal, T., Sundararajan, K., Fatima, A., Yan, Y., Xiang, Y., & Woodard, D. (2018) · ACM Computing Surveys · doi:10.1145/3132039 · Cited by 222 · see citing works
A Watermark for Large Language Models
Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., & Goldstein, T. (2023) · arXiv · doi:10.48550/arxiv.2301.10226 · Cited by 194 · see citing works
Do writing and speaking employ the same syntactic representations?
Cleland, A. A., & Pickering, M. J. (2006) · Journal of Memory and Language · doi:10.1016/j.jml.2005.10.003 · Cited by 174 · see citing works
On The Differences Between Spoken and Written Language
Akinnaso, F. N. (1982) · Language and Speech · doi:10.1177/002383098202500201 · Cited by 170 · see citing works
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
Mitchell, E., Lee, Y., Khazatsky, A., Manning, C. D., & Finn, C. (2023) · arXiv · doi:10.48550/arxiv.2301.11305 · Cited by 151 · see citing works
Using Compression-Based Language Models for Text Categorization
Teahan, W. J., & Harper, D. J. (2003) · Springer eBooks · doi:10.1007/978-94-017-0171-6_7 · Cited by 138 · see citing works
A Survey of Speaker Recognition: Fundamental Theories, Recognition Methods and Opportunities
Kabir, M. M., Mridha, M. F., Shin, J., Jahan, I., & Ohi, A. Q. (2021) · IEEE Access · doi:10.1109/access.2021.3084299 · Cited by 130 · see citing works
Who was student and why do we care so much about his t-test?1
Livingston, E. H. (2004) · Journal of Surgical Research · doi:10.1016/j.jss.2004.02.003 · Cited by 116 · see citing works
Burgoon, J. K., Mayew, W. J., Giboney, J. S., Elkins, A. C., Moffitt, K. C., Dorn, B., Byrd, M. D., & Spitzley, L. A. (2015) · Journal of Language and Social Psychology · doi:10.1177/0261927x15586792 · Cited by 94 · see citing works
Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation
Wang, C., Wu, Y., Liu, S., Yang, Z., & Zhou, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6452 · Cited by 83 · see citing works
ConvoKit: A Toolkit for the Analysis of Conversations
Chang, J. P., Chiam, C., Fu, L., Wang, A., Zhang, J., & Danescu-Niculescu-Mizil, C. (2020) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2020.sigdial-1.8 · Cited by 83 · see citing works
Discrimination of authorship using visualization
Kjell, B. P., Woods, W. A., & Frieder, O. (1994) · Information Processing & Management · doi:10.1016/0306-4573(94)90029-9 · Cited by 75 · see citing works
100,000 Podcasts: A Spoken English Document Corpus
Clifton, A., Reddy, S., Yu, Y., Pappu, A., Rezapour, R., Bonab, H., Eskevich, M., Jones, G. J. F., Jussi Karlgren, C., Carterette, B., et al. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.519 · Cited by 72 · see citing works
How Language Works: How Babies Babble, Words Change Meaning, and Languages Live or Die
Crystal, D. S. (2006) · Cited by 67 · see citing works
Authorship identification using ensemble learning
Abbasi, A., Javed, A. R., Iqbal, F., Jalil, Z., Gadekallu, T. R., & Kryvinska, N. (2022) · Scientific Reports · doi:10.1038/s41598-022-13690-4 · Cited by 66 · see citing works
Explainable Authorship Verification in Social Media via Attention-based Similarity Learning
Boenninghoff, B., Hessler, S., Kolossa, D., & Nickel, R. M. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005650 · Cited by 66 · see citing works
BertAA : BERT fine-tuning for Authorship Attribution.
Fabien, M., Villatoro-Tello, E., Motlíček, P., & Parida, S. (2020) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 64 · see citing works
Deepfake Text Detection: Limitations and Opportunities
Pu, J., Sarwar, Z., Abdullah, S. M., Rehman, A., Kim, Y., Bhattacharya, P., Javed, M., & Viswanath, B. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179387 · Cited by 62 · see citing works
Conversational Flow in Oxford-style Debates
Zhang, J., Kumar, R., Ravi, S., & Danescu-Niculescu-Mizil, C. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1017 · Cited by 61 · see citing works
Authorship verification for different languages, genres and topics
Halvani, O., Winter, C., & Pflug, A. (2016) · Digital Investigation · doi:10.1016/j.diin.2016.01.006 · Cited by 53 · see citing works
Speech Recognition and Multi-Speaker Diarization of Long Conversations
Mao, H. H., Li, S. X., McAuley, J., & Cottrell, G. W. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-3039 · Cited by 52 · see citing works
GRUEN for Evaluating Linguistic Quality of Generated Text
Zhu, W., & Bhat, S. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.9 · Cited by 50 · see citing works
Topic or style? Exploring the most useful features for authorship attribution
Sari, Y. A. P., Stevenson, M., & Vlachos, A. (2018) · Apollo (University of Cambridge) · doi:10.17863/cam.78746 · Cited by 48 · see citing works
On the State of the Art in Authorship Attribution and Authorship VerificationPreprint
Tyo, J., Dhingra, B., & Lipton, Z. C. (2022) · arXiv.org · doi:10.48550/arxiv.2209.06869 · Cited by 47 · see citing works
The British Academic Spoken English (BASE) Corpus Project
Thompson, P., & Nesi, H. (2001) · Language Teaching Research · doi:10.1177/136216880100500305 · Cited by 39 · see citing works
Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective
Uchendu, A., Le, T., & Lee, D. (2023) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3606274.3606276 · Cited by 34 · see citing works
Self-citationTie-breaker: Using language models to quantify gender bias in sports journalismPreprint
Fu, L., Danescu-Niculescu-Mizil, C., & Lee, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.03895 · Cited by 29 · see citing works
MGTBench: Benchmarking Machine-Generated Text DetectionPreprint
He, X., Shen, X., Chen, Z., Michael Backes, A., & Zhang, Y. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.14822 · Cited by 29 · see citing works
Overview of the Cross-Domain Authorship Verification Task at PAN 2020.
Kestemont, M., Manjavacas, E., Markov, I., Bevendorff, J., Wiegmann, M., Stamatatos, E., Potthast, M., & Stein, B. (2020) · CLEF (Working Notes) · Cited by 27 · see citing works
Asking too much? The rhetorical role of questions in political discourse
Zhang, J., Spirling, A., & Danescu-Niculescu-Mizil, C. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1164 · Cited by 25 · see citing works
Real-Time End-to-End Speech Emotion Recognition with Cross-Domain Adaptation
Wongpatikaseree, K., Singkul, S., Hnoohom, N., & Yuenyong, S. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6030079 · Cited by 23 · see citing works
Confessions of a Public Speaker
Berkun, S. (2009) · Cited by 18 · see citing works
Metadiscourse in Academic Written and Spoken English: A Comparative Corpus-Based Inquiry
Farahani, M. V. (2020) · Research in Language · doi:10.18778/1731-7533.18.3.05 · Cited by 12 · see citing works
Bevendorff, J., Chulvi, B., Fersini, E., Heini, A., Kestemont, M., Kredens, K., Mayerl, M., Ortega-Bueno, R., Pęzik, P., Potthast, M., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-99739-7_42 · Cited by 8 · see citing works
Characteristics of speaking style and implications for speech recognition
Shinozaki, T., Mari Ostendorf, E., & Atlas, L. (2009) · The Journal of the Acoustical Society of America · doi:10.1121/1.3183593 · Cited by 6 · see citing works
Feature Vector Difference based Authorship Verification for Open-World Settings.
Weerasinghe, J., Singh, R., & Greenstadt, R. (2021) · Cross-Language Evaluation Forum · Cited by 6 · see citing works
The Oyez Project: U.S. Supreme Court Multimedia Database
Urofsky, M. I. (2001) · Journal of American History · doi:10.2307/2675266 · Cited by 2 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
Research works printed but not linked: 10
- Ahmed Abbasi and Hsinchun Chen. 2008. Writeprints: Astylometric approach to identity-level identification and similarity detection in cyberspace. ACM Transactions on Information Systems (TOIS) , 26(2):1-29.
- Gillian Brown, Gillian D Brown, George Yule, Gillian R Brown, and Brown Gillian. 1983. Discourse analysis . Cambridge university press.
- BNC Consortium et al. 2007. British national corpus. Oxford Text Archive Core Collection .
- Howard Giles. 1973. Accent mobility: A model and some data. Anthropological linguistics , pages 87105.
- Hans Van Halteren. 2007. Author verification by linguistic profiling: An exploration of the parameter space. ACM Transactions on Speech and Language Processing (TSLP) , 4(1):1-17.
- Robbie Love, Claire Dembry, Andrew Hardie, Vaclav Brezina, and Tony McEnery. 2017. The spoken bnc2014: Designing and building a spoken corpus of everyday conversations. International Journal of Corpus Linguistics , 22(3):319-344.
- Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021. Mauve: Measuring the gap between neural text and human text using divergence frontiers. Advances in Neural Information Processing Systems , 34:4816-4828.
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog , 1(8):9.
- Melvin I Urofsky. 2001. The oyez project: Us supreme court multimedia database. The Journal of American History , 88(2):753.
- Jason S Wrench, James C McCroskey, and Virginia P Richmond. 2008. Human communication in everyday life: Explanations and applications . Pearson Education.
Cited by
Sorted by citations
A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
Wu, J., Yang, S., Zhan, R., Yuan, Y., Chao, L. S., & Wong, D. F. (2025) · Computational Linguistics · doi:10.1162/coli_a_00549 · Cited by 212 · see citing works
Blurring the lines: how AI is redefining artistic ownership and copyright
Watiktinnakorn, C., Seesai, J., & Kerdvibulvech, C. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00088-y · Cited by 58 · see citing works
Enhancing the Robustness of AI-Generated Text Detectors: A Survey
Liu, X., Li, Y., & Li, K. (2025) · Mathematics · doi:10.3390/math13132145 · Cited by 10 · see citing works
Can Authorship Attribution Models Distinguish Speakers in Speech Transcripts?
Aggazzotti, C., Andrews, N., & Smith, E. A. T. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00678 · Cited by 4 · see citing works
Cheng, Z., Zhou, L., Jiang, F., Wang, B., & Li, H. (2025) · Proceedings of the ACM on Web Conference 2025 · doi:10.1145/3696410.3714770 · Cited by 3 · see citing works
The Impact of Automatic Speech Transcription on Speaker Attribution
Aggazzotti, C., Wiesner, M., Smith, E. A., & Andrews, N. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.54 · Cited by 6 · see citing works
Mani, P., & Arulanandam, A. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1921847
- Authors
- Tripto, N., Uchendu, A., Le, T., Setzu, M., Giannotti, F., & Lee, D.
- Affiliations
- Pennsylvania State University (US)
- University of Mississippi (US)
- Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Published
- 2023
- Pages
- 13706-13724
- OpenAlex
- W4389518717
- Cited by
- 6
- References
- 69
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2023: 1
- FWCI
- 1.07
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion, cc-by-nc-sa)
- Scuola Normale Superiore di Pisa (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Science Foundation (1820609, 1934782, 2114824, 2131144)
- European Commission (654024, IR0000013, HORIZON2020, PE00000013, 101079043, 871042, Spoke 1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- authorship analysis, stylometry, spoken text, AI-generated text detection, authorship attribution, author verification, large language models, speech dataset
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
