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and DoD fudning. Opinions, interpretations, conclusions and recommendations are those of the authors and are not necessarily endorsed by the United States Government + The authors gratefully acknowledge the CLSP group at JHU for organizing and hosting WS2002. ABSTRACT The area of automatic speaker recognition has been dominated by systems using only(More)
Our feature extraction module for the Aurora task is based on a combination of a conventional noise supression technique (Wiener filtering) with our temporal processing tech-nigues (linear discriminant RASTA filtering and nonlinear TempoRAl Pattern (TRAP) classifier). We observe better than 58% relative error improvement on the prescribed Au-rora Digit(More)
Most current state-of-the-art automatic speaker recognition systems extract speaker-dependent features by looking at short-term spectral information. This approach ignores long-term information that can convey supra-segmental information, such as prosodics and speaking style. We propose two approaches that use the fundamental frequency and energy(More)