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Most of the current automatic speech-based cognitive load measurement systems utilize acoustic features estimated using a mel filterbank. However, a previous study showed that a non-uniform filterbank designed specifically to emphasize cognitive load information present in low frequencies was more effective than a mel filterbank under noise-free conditions. This paper investigates the effectiveness...
Tracking concept drifts in data streams has recently become a hot topic in data mining. Most of the existing work is built on a single-window-based mechanism to detect concept drifts. Due to the inherent limitation of the single-window-based mechanism, it is a challenge to handle different types of drifts. Motivated by this, a new classification algorithm based on a double-window mechanism for handling...
Automatic recognition of emotional states via speech signal has attracted increasing attention in recent years. A number of techniques have been proposed which are capable of providing reasonably high accuracy for controlled studio settings. However, their performance is considerably degraded when the speech signal is contaminated by noise. In this paper, we present a framework with adaptive noise...
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