Text-independent Speaker Verification

In this chapter, an overview of text-independent speaker verification is given first. Then, recent developments needed to reach state-of-the-art performances using low-level (acoustic) features as well as how to use complementary high-level information, are presented. The most relevant speaker verification evaluation campaigns and databases are also summarized. The BioSecure benchmarking framework for speaker verification using open-source state-of-the-art algorithms, well-known databases, and reference protocols is presented after. It is also shown how to reach state-of-the-art performances using open-source software with a case study example on the National Institute of Standards and Technology 2005 Speaker Evaluation data (NIST’2005 SRE). The examples of key factors influencing the performances of speaker verification experiments on the NIST’2005 evaluation data are grouped in three parts. The first set of experiments is related to the importance of front-end processing and data selection to fine-tune the acoustic Gaussian Mixture systems. The second set of experiments illustrates the importance of speaker and session variability modeling methods in order to cope with mismatched enrollment/test conditions. The third series of experiments demonstrates the usefulness of data-driven speech segmentation methods for extracting complementary high-level information. The chapter ends with conclusions and perspectives.