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It has been shown that standard cepstral speaker recognition models can be enhanced by region-constrained models, where features are extracted only from certain speech regions defined by linguistic or prosodic criteria. Such region-constrained models can capture features that are more stable, highly idiosyncratic, or simply complementary to the baseline system. In this paper we ask if another major...
The SRI speaker recognition system for the 2010 NIST speaker recognition evaluation (SRE) incorporates multiple subsystems with a variety of features and modeling techniques. We describe our strategy for this year's evaluation, from the use of speech recognition and speech segmentation to the individual system descriptions as well as the final combination. Our results show that under most conditions,...
Constrained cepstral systems, which select frames to match various linguistic “constraints” in enrollment and test, have shown significant improvements for speaker verification performance. Past work, however, relied on word recognition, making the approach language dependent (LD). We develop language-independent (LI) versions of constraints and compare results to parallel LD versions for English...
We investigate using state-of-the-art speaker diarization output for speech recognition purposes. While it seems obvious that speech recognition could benefit from the output of speaker diarization (“Who spoke when”) for effective feature normalization and model adaptation, such benefits have remained elusive in the very challenging domain of meeting recognition from distant microphones. In this study,...
We compare two recently proposed techniques, within class covariance normalization (WCCN) [1] and nuisance attribute projection (NAP) [2], for intersession variability compensation in speaker verification. The comparison is performed using an MLLR-SVM speaker verification system. Both techniques model intersession variability using a within-speaker covariance matrix (WSCM). However, they manipulate...
<para> We present a new modeling approach for speaker recognition that uses the maximum-likelihood linear regression (MLLR) adaptation transforms employed by a speech recognition system as features for support vector machine (SVM) speaker models. This approach is attractive because, unlike standard frame-based cepstral speaker recognition models, it normalizes for the choice of spoken words...
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