Cited by 23Authorship Attribution and ProfilingUser Authentication and Security SystemsSpeech Recognition and Synthesis

Person Identification from Text and Speech Genre Samples

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Goldstein-Stewart, Jade, Winder, Ransom, Sabin, Roberta Evans·2009·Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics on - EACL '09

Abstract

In this paper, we describe experiments conducted on identifying a person using a novel unique correlated corpus of text and audio samples of the person's communication in six genres. The text samples include essays, emails, blogs, and chat. Audio samples were collected from individual interviews and group discussions and then transcribed to text. For each genre, samples were collected for six topics. We show that we can identify the communicant with an accuracy of 71% for six fold cross validation using an average of 22,000 words per individual across the six genres. For person identification in a particular genre (train on five genres, test on one), an average accuracy of 82% is achieved. For identification from topics (train on five topics, test on one), an average accuracy of 94% is achieved. We also report results on identifying a person's communication in a genre using text genres only as well as audio genres only.

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Crossref · 10.3115/1609067.1609104

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  • The Linguistic Inquiry and Word Count (LIWC) tool uses more than 100 psychological dimensions to analyze word use, reflecting distinct language variations among different groups in specific genres and languages.

    “The Linguistic Inquiry and Word Count (LIWC) tool exemplifies how automated text analysis can use more than 100 psychological dimensions to analyze word use, reflecting distinct language variations among different groups in specific genres and languages [Goldstein-Stewart et al., 2009;Pennebaker et al., 2015;Dudău and Sava, 2021].”

    From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Goldstein-Stewart et al., 2009

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Authors
Goldstein-Stewart, J., Winder, R., & Sabin, R. E.
Affiliations
Loyola University Maryland (US)
Venue
Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics on - EACL '09
Published
2009
Pages
336-344
OpenAlex
W2091612886
Cited by
22
References
28
Citations by year
2025: 1 · 2024: 1 · 2023: 4 · 2022: 3 · 2021: 4 · 2020: 2 · 2019: 1 · 2018: 1 · 2017: 2 · 2016: 2 · 2015: 1
FWCI
0.00
Type
conference paper
Language
en
Open access
Locations
Conference of the European Chapter of the Association for Computational Linguistics (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
  • Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Keywords
speaker identification, personal identification, textual genres, discursive genres, chat, group discussion

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