Study on Inter and Intra Speaker Variability in Speaker Recognition

TL;DR

This paper analyzes how inter- and intra-speaker variability affect neural network-based speaker recognition systems and provides guidelines for data collection, including releasing metadata to improve research practices.

Contribution

It offers an analysis of speaker variability dependencies in training data and releases utterance upload date metadata for the VoxTube dataset to aid data collection practices.

Findings

Dependency between inter- and intra-speaker variability analyzed

Release of upload date metadata for VoxTube dataset

Guidelines for data collection and filtering

Abstract

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.