HYU Submission for the SASV Challenge 2022: Reforming Speaker Embeddings with Spoofing-Aware Conditioning
- Authors
- Choi, Jeong-Hwan; Yang, Joon-Young; Jeoung, Ye-Rin; Chang, Joon-Hyuk
- Issue Date
- Sep-2022
- Publisher
- ISCA-INT SPEECH COMMUNICATION ASSOC
- Keywords
- speaker verification; anti-spoofing; spoofing-aware speaker verification
- Citation
- INTERSPEECH 2022, pp.2873 - 2877
- Indexed
- SCOPUS
- Journal Title
- INTERSPEECH 2022
- Start Page
- 2873
- End Page
- 2877
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/184927
- DOI
- 10.21437/Interspeech.2022-210
- ISSN
- 2308-457X
- Abstract
- In this paper, we introduce the spoofing-aware speaker verification (SASV) system submitted by the Hanyang University team for SASV Challenge 2022. Our strategy is to learn spoofing-aware speaker embeddings (SASEs) that can effectively produce SASV scores by using a simple cosine similarity scoring backend. To achieve this, we develop a neural-network-based SASE model that uses a spoofing countermeasure (CM) embedding and speaker embedding to produce an SASE. The baseline anti-spoofing model is used to extract CM embeddings, and ResNet-34- and Res2Net-based models are employed to extract speaker embeddings. When evaluated on the ASVspoof2019 logical access dataset, our best proposed SASV system achieved SASV equal error rates of 0.1817% and 0.2793% on the development and evaluation set partitions, respectively, placing 3rd in the SASV Challenge 2022.
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