Eric H. C. Choi

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Multimodal user interfaces (MMUI) allow users to control computers using speech and gesture, and have the potential to minimise users. experienced cognitive load, especially when performing complex tasks. In this paper, we describe our attempt to use a physiological measure, namely Galvanic Skin Response (GSR), to objectively evaluate users. stress and(More)
Extensive research has been devoted to robustness in the presence of various types and degrees of environmental noise over the past several years, however this remains one of the main problems facing automatic speech recognition systems. This paper describes a new variable frame rate analysis technique, based upon searching a predefined lookahead interval(More)
Speech is a promising modality for the convenient measurement of cognitive load, and recent years have seen the development of several cognitive load classification systems. Many of these systems have utilised mel frequency cepstral coefficients (MFCC) and prosodic features like pitch and intensity to discriminate between different cognitive load levels.(More)
High cognitive load arises from complex time and safety-critical tasks, for example, mapping out flight paths, monitoring traffic, or even managing nuclear reactors, causing stress, errors, and lowered performance. Over the last five years, our research has focused on using the multimodal interaction paradigm to detect fluctuations in cognitive load in user(More)
Multimodal interfaces are known to be useful in map-based applications, and in complex, time-pressure based tasks. Cognitive load variations in such tasks have been found to impact multimodal behaviour. For example, users become more multimodal and tend towards semantic complementarity as cognitive load increases. The richness of multimodal data means that(More)
Due to the increasing use of fusion in speaker recognition systems, features that are complementary to MFCCs offer opportunities to advance the state of the art. One promising feature is based on group delay, however this can suffer large variability due to its numerical formulation. In this paper, we investigate reducing this variability in group delay(More)