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In the present study we investigated the effects of anodal transcranial direct current stimulation over the auditory cortex (AC) on the perception of rapidly changing acoustic cues. For this purpose, in 15 native German speakers the left or right AC was separately stimulated while participants performed a between-channel gap detection task. Results show(More)
Missing data techniques have been recently applied to speaker recognition to increase performance in noisy environments. The drawback of these techniques is the vulnerability of the recognizer to errors in the classification of time-frequency points as corrupt or reliable. In this paper we propose the combination of missing data processing and feature(More)
The aim of this work was to determine the influence of different processing procedures and preparations on the viability and infectivity of Trichinella spiralis ML. The muscles of limbs tongue and masseters of pigs experimentally infected were collected, splitted to pieces, and pooled. Five batches were used for the following processing procedures: (1)(More)
Successful localization of sound sources in reverberant enclosures is an important prerequisite for many spatial signal processing algorithms. We investigate the use of a weighted fuzzy <b>c</b>-means cluster algorithm for robust source localization using location cues extracted from a microphone array. In order to increase the algorithm's robustness(More)
Conventional hidden Markov model (HMM) decoders often experience severe performance degradations in practice due to their inability to cope with uncertain data in time-varying environments. In order to address this issue, we propose the bounded-Gauss-Uniform mixture probability density function (pdf) as a new class of evidence model for missing data speech(More)
This paper adopts the framework of DUET, a recently proposed blind source separation (BSS) method, for speech recognition. Based on the attenuation and delay estimation in stereo signals spectrographic masks are designed to extract a target speaker from a mixture containing multiple speech sources. Instead of using these masks for resynthesis we avoid(More)
This paper investigates the use of DUET, a recently proposed blind source separation method, as front-end for missing data speech recognition. Based on the attenuation and delay estimation in stereo signals soft time-frequency masks are designed to extract a target speaker from a mixture containing multiple speech sources. A postprocessing step is(More)
This work investigates the use of missing data techniques for noise robust speaker identification. Most previous work in this field relies on the diagonal covariance assumption in modeling speaker specific characteristics via Gaussian mixture models. This paper proposes the use of full covariance models that can capture linear correlations among feature(More)