Publications

This paper presents a description of the MIT Lincoln Laboratory submissions to the 2005 NIST Language Recognition Evaluation (LRE05). As was true in 2003, the 2005 submissions were combinations of core cepstral and phonotactic recognizers whose outputs were fused to generate final scores. For the 2005 evaluation, Lincoln Laboratory had...

Speaker recognition using high-level features has been a successful area of exploration. Features obtained from many different levels phones, words, prosodic events, etc. are used to characterize the speaker. A good modeling technique for these features is the support vector machine (SVM). SVMs model the n-gram frequencies from speaker utterances...

In this paper we describe experiments conducted during the development of a lattice-based PPRLM language identification system as part of the NIST 2005 language recognition evaluation campaign. In experiments following LRE05 the PPRLM-lattice sub-system presented here achieved a 30s/primary condition EER of 4.87%, making it the single best performing recognizer...

Recent work in forensic speaker recognition has introduced many new scoring methodologies. First, confidence scores (posterior probabilities) have become a useful method of presenting results to an analyst. The introduction of an objective measure of confidence score quality, the normalized cross entropy, has resulted in a systematic manner of evaluating...

Gaussian mixture models with universal backgrounds (UBMs) have become the standard method for speaker recognition. Typically, a speaker model is constructed by MAP adaptation of the means of the UBM. A GMM supervector is constructed by stacking the means of the adapted mixture components. A recent discovery is that latent...

Gaussian mixture models (GMMs) have proven extremely successful for text-independent speaker recognition. The standard training method for GMMmodels is to use MAP adaptation of the means of the mixture components based on speech from a target speaker. Recent methods in compensation for speaker and channel variability have proposed the idea...

Support vector machines (SVMs) have proven to be a powerful technique for pattern classification. SVMs map inputs into a high-dimensional space and then separate classes with a hyperplane. A critical aspect of using SVMs successfully is the design of the inner product, the kernel, induced by the high dimensional mapping...

The intelligibility of speech transmitted through low-rate coders is severely degraded when high levels of acoustic noise are present in the acoustic environment. Recent advances in nonacoustic sensors, including microwave radar, skin vibration, and bone conduction sensors, provide the exciting possibility of both glottal excitation and, more generally, vocal tract...

The MIT Lincoln Laboratory submission for the 2004 NIST Speaker Recognition Evaluation (SRE) was built upon seven core systems using speaker information from short-term acoustics, pitch and duration prosodic behavior, and phoneme and word usage. These different levels of information were modeled and classified using Gaussian Mixture Models, Support Vector...

Cross-channel degradation is one of the significant challenges facing speaker recognition systems. We study the problem for speaker recognition using support vector machines (SVMs). We perform channel compensation in SVM modeling by removing non-speaker nuisance dimensions in the SVM expansion space via projections. Training to remove these dimensions is accomplished...