# C-P Map: A Novel Evaluation Toolkit for Speaker Verification

arXiv: https://arxiv.org/abs/2203.02942 · HTML: https://mlsanity.com/paper/2203.02942/

## 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.

## Placed in

- Speech and audio language models / [Speaker recognition and diarization](https://mlsanity.com/speech/speaker-recognition-and-diarization/)

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [CN-CELEB: a challenging Chinese speaker recognition dataset](https://mlsanity.com/paper/1911.01799/) (2019)
- [Speaker Recognition Based on Deep Learning: An Overview](https://mlsanity.com/paper/2012.00931/) (2020)
- [CN-Celeb: multi-genre speaker recognition](https://mlsanity.com/paper/2012.12468/) (2020)
- [VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2012.06867/) (2020)
- [VoxSRC 2019: The first VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/1912.02522/) (2019)
- [HI-MIA : A Far-field Text-Dependent Speaker Verification Database and the Baselines](https://mlsanity.com/paper/1912.01231/) (2019)
- [ResNeXt and Res2Net Structures for Speaker Verification](https://mlsanity.com/paper/2007.02480/) (2020)

## Built on by (most cited first, 3 of 3)

- [Reliable Visualization for Deep Speaker Recognition](https://mlsanity.com/paper/2204.03852/) (2022)
- [Pay Attention to Hard Trials](https://mlsanity.com/paper/2209.04687/) (2022)
- [VoxAnon: A balanced dataset for a fair evaluation of speech anonymization](https://mlsanity.com/paper/2610.13313/) (2026)

## 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.