Spear: An open source toolbox for speaker recognition based on Bob

In this paper, we introduce Spear, an open source and extensible toolbox for state-of-the-art speaker recognition. This toolbox is built on top of Bob, a free signal processing and machine learning library. Spear implements a set of complete speaker recognition toolchains, including all the processing stages from the front-end feature extractor to the final steps of decision and evaluation. Several state-of-the-art modeling techniques are included, such as Gaussian mixture models, inter-session variability, joint factor analysis and total variability (i-vectors). Furthermore, the toolchains can be easily evaluated on well-known databases such as NIST SRE and MOBIO. As a proof of concept, an experimental comparison of different modeling techniques is conducted on the MOBIO database.