UMBC Office of Institutional Advancement

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http://hdl.handle.net/11603/25505
UMBC’s Office of Institutional Advancement supports the University’s mission and strategic priorities in a variety of ways, including telling the UMBC story through brand, stories, and design; building partnerships with community stakeholders; and fundraising to support students, faculty, alumni, and programs.

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Recent Submissions

Now showing 1 - 5 of 5
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    Investigating Causal Cues: Strengthening Spoofed Audio Detection with Human-Discernible Linguistic Features
    (2024-09-09) Khanjani, Zahra; Ale, Tolulope; Wang, Jianwu; Davis, Lavon; Mallinson, Christine; Janeja, Vandana
    Several types of spoofed audio, such as mimicry, replay attacks, and deepfakes, have created societal challenges to information integrity. Recently, researchers have worked with sociolinguistics experts to label spoofed audio samples with Expert Defined Linguistic Features (EDLFs) that can be discerned by the human ear: pitch, pause, word-initial and word-final release bursts of consonant stops, audible intake or outtake of breath, and overall audio quality. It is established that there is an improvement in several deepfake detection algorithms when they augmented the traditional and common features of audio data with these EDLFs. In this paper, using a hybrid dataset comprised of multiple types of spoofed audio augmented with sociolinguistic annotations, we investigate causal discovery and inferences between the discernible linguistic features and the label in the audio clips, comparing the findings of the causal models with the expert ground truth validation labeling process. Our findings suggest that the causal models indicate the utility of incorporating linguistic features to help discern spoofed audio, as well as the overall need and opportunity to incorporate human knowledge into models and techniques for strengthening AI models. The causal discovery and inference can be used as a foundation of training humans to discern spoofed audio as well as automating EDLFs labeling for the purpose of performance improvement of the common AI-based spoofed audio detectors.
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    A place for (socio)linguistics in audio deepfake detection and discernment: Opportunities for convergence and interdisciplinary collaboration
    (Wiley, 2024-07-09) Mallinson, Christine; Janeja, Vandana; Evered, Chloe; Khanjani, Zahra; Davis, Lavon; Bhalli, Noshaba Nasir; Nwosu, Kifekachukwu
    Deepfakes, particularly audio deepfakes, have become pervasive and pose unique, ever-changing threats to society. This paper reviews the current research landscape on audio deepfakes. We assert that limitations of existing approaches to deepfake detection and discernment are areas where (socio)linguists can directly contribute to helping address the societal challenge of audio deepfakes. In particular, incorporating expert knowledge and developing techniques that everyday listeners can use to avoid deception are promising pathways for (socio)linguistics. Further opportunities exist for developing benevolent applications of this technology through generative AI methods as well.
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    The Hilltop Institute at UMBC revolutionizes data analytics to advance health and wellbeing
    (UMBC News, 2022-08-31) Duque, Catalina Sofia Dansberger