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In this paper, we show how a model of human cognition based on ACT-R can be improved to accurately predict cognitive performance under different workload levels. For this purpose, we propose a novel approach which uses an EEG-based workload model to (de-)activate a dummy model which runs in parallel to the actual task model. The dummy model consumes cognitive resources to reflect the effect of workload...
In this paper, we show that multiple operations of the typical pattern recognition chain of an fNIRS-based BCI, including feature extraction and classification, can be unified by solving a convex optimization problem. We formulate a regularized least squares problem that learns a single affine transformation of raw HbO2 and HbR signals. We show that this transformation can achieve competitive results...
In this paper, we evaluate grapheme-to-phoneme (g2p) models among languages and of different quality. We created g2p models for Indo-European languages with word-pronunciation pairs from the GlobalPhone project and from Wiktionary [1]. Then we checked their quality in terms of consistency and complexity as well as their impact on Czech, English, French, Spanish, Polish, and German ASR. While the GlobalPhone...
This paper presents our work on rapid language adaptation of acoustic models based on multilingual cross-language bootstrapping and unsupervised training. We used Automatic Speech Recognition (ASR) systems in English, French, German, and Spanish to build a Czech ASR system from scratch. System building was performed without using any transcribed audio data by applying three consecutive steps, i.e...
Far-field speaker identification is very challenging since varying recording conditions often result in un-matching training and testing situations. Although the widely used Gaussian Mixture Models (GMM) approach achieves reasonable good results when training and testing conditions match, its performance degrades dramatically under un-matching conditions. In this paper we propose a new approach for...
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