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CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking
Open accessAbstract
Time delay neural network (TDNN) has been proven to be efficient for speaker verification. One of its successful variants, ECAPA-TDNN, achieved state-of-the-art performance at the cost of much higher computational complexity and slower inference speed. This makes it inadequate for scenarios with demanding inference rate and limited computational resources. We are thus interested in finding an architecture that can achieve the performance of ECAPA-TDNN and the efficiency of vanilla TDNN. In this paper, we propose an efficient network based on context-aware masking, namely CAM++, which uses densely connected time delay neural network (D-TDNN) as backbone and adopts a novel multi-granularity pooling to capture contextual information at different levels. Extensive experiments on two public benchmarks, VoxCeleb and CN-Celeb, demonstrate that the proposed architecture outperforms other mainstream speaker verification systems with lower computational cost and faster inference speed.
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“The ASV model is the CAM++ model from Wang et al. (2023). It is chosen only for the reasons that it is different to the baseline ASV system described in Section 7.1 and that its use hence supports evaluation in a black-box setting.”
From ASVspoof 5: Design, collection and validation of resources for spoofing, deepfake, and adversarial attack detection using crowdsourced speech (2026) · cites this paper as Wang et al. (2023)
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- Y.-Q. Yu, S. Zheng, H. Suo, Y. Lei, and W.-J. Li, "Cam: Context- aware masking for robust speaker verification," in IEEE Interna- tional Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 6703-6707.
- B. Liu, Z. Chen, S. Wang, H. Wang, B. Han, and Y. Qian, "Df- resnet: Boosting speaker verification performance withdepth-first design," in Annual Conference of the International Speech Com- munication Association (INTERSPEECH), 2022, pp. 296-300.
- C. Tan, Q. Chen, W. Wang, Q. Zhang, S. Zheng, and Z. Ling, "Ponet: Pooling network for efficient token mixing in long se- quences," in The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022.
- M. Indiay, P. Safariy, and J. Hernando, "Self multi-head atten- tion for speaker recognition," in Annual Conference of the Inter- national Speech Communication Association (INTERSPEECH), 2019, pp. 4305-4309.
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Huang, Z., Lin, J., Ge, M., Jiang, Y., Wang, X., Wang, L., & Dang, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889137 · Cited by 2
SCDiar: a streaming diarization system based on speaker change detection and speech recognition
Zheng, N., Wan, X., Liu, K., & Zhou, H. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888692 · Cited by 2
Wu, X., Sun, H., Huang, D., Yin, X., Wang, Y., Wang, H., Zhang, J., Wang, F., Guo, P., Xing, S., et al. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3761980 · Cited by 2
Self-citationMitigating Hallucinations in LM-Based TTS Models via Distribution Alignment Using GFlowNets
Liu, C., Fang, M., Zhang, P., Zhou, W., Gao, J., & Han, J. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.976 · Cited by 2
Zhang, Y., Su, H., Fan, L., Luo, Z., & Luan, J. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2509.15612 · Cited by 2
R2-SVC: Towards Real-World Robust and Expressive Zero-shot Singing Voice ConversionPreprint
Zheng, J., Chen, G., Ding, C., & Chen, Z. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2510.20677 · Cited by 2
Wu, X., Huang, D., Sun, H., Yin, X., Wang, Y., Wang, H., Zhang, J., Wang, F., Guo, P., Xing, S., et al. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2507.22781 · Cited by 2
Self-citationVITA-QinYu: Expressive Spoken Language Model for Role-Playing and SingingPreprint
Xu, J., Gao, H., Xie, L., Yang, Z., Li, L., Chen, Y., Zhang, B., Chen, M., Fu, C., Zhao, W., et al. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2605.06765 · Cited by 2
Self-citationLiu, M., Li, Y., Zhao, H., Bian, Y., Ma, J., Zhang, J., Chen, J., Deng, X., & Wang, Z. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2607.28165 · Cited by 2
Metadata
- Authors
- Wang, H., Zheng, S., Chen, Y., Cheng, L., & Chen, Q.
- Venue
- INTERSPEECH 2023
- Publisher
- ISCA
- Published
- 2023-08-20
- Pages
- 5301-5305
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W4385823321
- Cited by
- 150
- References
- 0
- Citations by year
- 2026: 47 · 2025: 76 · 2024: 24 · 2023: 3
- FWCI
- 26.58
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Interspeech (USB) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- speaker verification, CAM, attention mechanism, deep learning architectures
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 ›
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