# Study on Inter and Intra Speaker Variability in Speaker Recognition

arXiv: https://arxiv.org/abs/2411.07754 · HTML: https://mlsanity.com/paper/2411.07754/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Placed in

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

## Builds on (most cited first, 5 of 5)

- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [VoxBlink: A Large Scale Speaker Verification Dataset on Camera](https://mlsanity.com/paper/2308.07056/) (2023)
- [The DKU-MSXF Speaker Verification System for the VoxCeleb Speaker Recognition Challenge 2023](https://mlsanity.com/paper/2308.08766/) (2023)
- [The FFSVC 2020 Evaluation Plan](https://mlsanity.com/paper/2002.00387/) (2020)
- [Rethinking Session Variability: Leveraging Session Embeddings for Session Robustness in Speaker Verification](https://mlsanity.com/paper/2309.14741/) (2023)

## Abstract (arXiv)

Optimization of a trade-off between the number of speakers and their temporal variability (or session diversity) is crucial for the development of a speaker recognition system together with making the data collection process feasible from a time perspective. In this article, we provide the analysis of dependency between inter and intra speaker variability in training data for the modern neural network-based speaker recognition system using the VoxTube dataset for text-independent speaker recognition task. Besides, an auxiliary contribution of this work is a release of upload date metadata per utterance in a VoxTube dataset. We want this article to contribute to guidelines and best practices for collecting and filtering data from media hosting platforms to facilitate the efforts of researchers in developing speaker recognition systems.