In this paper we describe the major elements of MIT Lincoln Laboratory’s Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker representation, and a form of Bayesian adaptation to derive speaker models from the UBM. The development and use of a handset detector and score normalization to greatly improve verification performance is also described and discussed. Finally, representative performance benchmarks and system behavior experiments on NIST SRE corpora are presented. © 2000 Academic Press Key Words: speaker recognition; Gaussian mixture models; likelihood ratio detector; universal background model; handset normalization; NIST evaluation. 1.

Cohort normalization is a method for normalizing the scores in speaker verification in order to reduce undesirable variation arising from acoustically mismatched conditions. A particular form of cohort normalization, unconstrained cohort normalization (UCN) is addressed in this study. The UCN method has been shown to give excellent results but its major drawback is the huge computational load arising from the search of the cohort speakers. In this paper, we propose a fast cohort search algorithm, that quantizes the test vector sequence and uses the quantized data for both impostor and claimant scoring. Results on the NIST-1999 corpus show a speed-up factor of 23:1 compared to full search. Furthermore, the equal error rates are decreased from those of the full search. 1.

A Universal Background Model (UBM) is a model used in a biometric verification system to represent general, personindependent feature characteristics to be compared against a model of person-specific feature characteristics when making an accept or reject decision. For example, in a speaker verification system, the UBM is a speaker-independent Gaussian Mixture Model (GMM) trained with speech samples from a large set of speakers to represent general speech characteristics. Using a speaker-specific GMM trained with speech samples from a particular enrolled speaker, a likelihood-ratio test for an unknown speech sample can be formed between the match score of the speaker-specific model and the UBM. The UBM may also be used when training the speaker-specific model by acting as a the prior model in MAP parameter estimation.