Publication: CL-UZH submission to the NIST SRE 2024 Speaker Recognition Evaluation
CL-UZH submission to the NIST SRE 2024 Speaker Recognition Evaluation
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Faradi Pour, A., Shiran Liu, Chapariniya, M., Vyshnevetska, V., Vukovic, T., Dellwo, V., & Madikeri Raghunathan Srikanth. (2025, September 30). CL-UZH submission to the NIST SRE 2024 Speaker Recognition Evaluation. NIST SRE 2024, USA.
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The CL-UZH team submitted one system each for the fixed and open conditions of the NIST SRE 2024 challenge. For the closed-set condition, results for the audio-only trials were achieved using the X-vector system developed with Kaldi. For the audio-visual results we used only models developed for the visual modality. Two sets of results were submitted for the open-set and closed-set conditions, one based on a pretrained model using the VoxBlink2 and VoxCeleb2 datasets. An Xvector-based model was trained from scratch using the CTS super
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Faradi Pour, A., Shiran Liu, Chapariniya, M., Vyshnevetska, V., Vukovic, T., Dellwo, V., & Madikeri Raghunathan Srikanth. (2025, September 30). CL-UZH submission to the NIST SRE 2024 Speaker Recognition Evaluation. NIST SRE 2024, USA.
