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

Abstract not found

For large population speaker identification (SID) systems, likelihood computations between an unknown speaker’s test feature set and speaker models can be very time-consuming and detrimental to applications where fast SID is required. In this paper, we propose a method whereby speaker models

In large population speaker identification (SI) systems, like-lihood computations between an unknown speaker’s feature set and the registered speaker models can be very time-consuming and impose a bottleneck. For applications requir-ing fast SI, this is a problem. In prior work, we proposed the use

. To address this issue, a predetermined generic “speaker-independent ” model set, called Sample Speaker Models (SSM), was previously proposed. While this set can be useful for more accurate speaker modeling and clustering without any target speaker models, an optimal method for sampling the models from such a

Abstract—In large population speaker identification (SI) systems, likelihood computations between an unknown speaker’s feature vectors and the registered speaker models can be very time-consuming and impose a bottleneck. For applications requiring fast SI, this is a recognized problem

for speaker verification called OSCILLO. By applying tolerance interval analysis in statistics, OSCILLO can verify a speaker's ID without background speaker models. This greatly reduces the space requirement of the system and the time for both training and verification. Experimental results show

of the constrained model-space transform from the simple diagonal case to the full or block-diagonal case. The constrained and unconstrained transforms are evaluated in terms of computational cost, recognition time efficiency, and use for speaker adaptive training. The recognition performance of the two model

The generation of words in speech involves a number of processing stages. There is, first, a stage of conceptual preparation; this is followed by stages of lexical selection, phonological encoding, phonetic encoding and articulation. In addition, the speaker monitors the output and, if necessary

understood what the speaker meant well enough for current purposes. This is accomplished by the collective actions of the current contributor and his or her partners, and these result in units of conversation called contributions. We present a model of contributions and show how it accounts for o variety