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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)
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(More)
We present three voice activity detection (VAD) algorithms that are suitable for the off-line processing of noisy speech and compare their performance on SPINE-2 evaluation data using speech recognition error rate as the quality metric. One VAD system is a simple HMM-based segmenter that uses normalized log-energy and a degree of voicing measure as raw(More)
Using EEG signals to estimate cognitive state has drawn increasing attention in recently years, especially in the context of brain−computer interface (BCI) design. How−ever, this goal is extremely difficult because, in addition to the complex relationships between the cognitive state and EEG signals that yields the non−stationarity of the features extracted(More)