C-P Map: A Novel Evaluation Toolkit for Speaker Verification
On the map: Speech and audio language models → Speaker recognition and diarization
Abstract (arXiv)
Evaluation trials are used to probe performance of automatic speaker verification (ASV) systems. In spite of the clear importance and impact, evaluation trials have not been seriously treated in research and engineering practice. This paper firstly presents a theoretical analysis on evaluation trials and highlights potential bias with the most popular cross-pairing approach used in trials design. To interpret and settle this problem, we define the concept of trial config and C-P map derived from it. The C-P map measures the performance of an ASV system on various trial configs in a 2-dimensional map. On the map, each location represents a particular trial config and its corresponding color represents the system performance. Experiments conducted on representative ASV systems show that the proposed C-P map offers a powerful evaluation toolkit for ASV performance analysis and comparison. The source code for C-P map has been release at https://gitlab.com/csltstu/sunine.
Key facts. Cited by 3 papers in the map (data through 19 Oct 2026). Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models. Builds on 13 of its 30 references that are in the map. First posted on arXiv 6 Mar 2022.
Builds on
13 of 30 references.
References · 13
- 2015Deep Residual Learning for Image Recognitioncited by 52,456
- 2018VoxCeleb2: Deep Speaker Recognitioncited by 1,166
- 2017VoxCeleb: a large-scale speaker identification datasetcited by 985
- 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 980
- 2018Additive Margin Softmax for Face Verificationcited by 326
- 2018Attentive Statistics Pooling for Deep Speaker Embeddingcited by 207
- 2019CN-CELEB: a challenging Chinese speaker recognition datasetcited by 123
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- 2020VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challengecited by 46
- 2019VoxSRC 2019: The first VoxCeleb Speaker Recognition Challengecited by 25
- 2019HI-MIA : A Far-field Text-Dependent Speaker Verification Database and the Baselinescited by 23
1 more
- 2020ResNeXt and Res2Net Structures for Speaker Verificationcited by 20
Built on by
Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2022Reliable Visualization for Deep Speaker Recognitioncited by 7
- 2022Pay Attention to Hard Trialscited by 1
- 2026VoxAnon: A balanced dataset for a fair evaluation of speech anonymizationcited by 0
- 2026VoxAnon: A balanced dataset for a fair evaluation of speech anonymization9 Oct 2026
- 2022Pay Attention to Hard Trials10 Sep 2022
- 2022Reliable Visualization for Deep Speaker Recognition8 Apr 2022
Similar, not cited
Close in wording, no citation either way
- 2022Design Guidelines for Inclusive Speaker Verification Evaluation Datasets
- 2024As Biased as You Measure: Methodological Pitfalls of Bias Evaluations in Speaker Verification Research
- 2024a-DCF: an architecture agnostic metric with application to spoofing-robust speaker verification
- 2026Kiwano: A Cutting-Edge Open-Source Toolkit for Speaker Verification
- 2025SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions
- 2024Asymmetric and trial-dependent modeling: the contribution of LIA to SdSV Challenge Task 2
- 2024A Comparison of Differential Performance Metrics for the Evaluation of Automatic Speaker Verification Fairness
- 2022Representation Selective Self-distillation and wav2vec 2.0 Feature Exploration for Spoof-aware Speaker Verification