Evidence for on-line visual guidance during saccadic gaze shifts.

Rapid orientating movements of the eyes are believed to be controlled ballistically. The mechanism underlying this control is thought to involve a comparison between the desired displacement of the eye and an estimate of its actual position (obtained from the integration of the eye velocity signal). This study shows, however, that under certain circumstances fast gaze movements may be controlled quite differently and may involve mechanisms which use visual information to guide movements prospectively. Subjects were required to make large gaze shifts in yaw towards a target whose location and motion were unknown prior to movement onset. Six of those tested demonstrated remarkable accuracy when making gaze shifts towards a target that appeared during their ongoing movement. In fact their level of accuracy was not significantly different from that shown when they performed a 'remembered' gaze shift to a known stationary target (F-3,F-15 = 0.15, p > 0.05). The lack of a stereotypical relationship between the skew of the gaze velocity profile and movement duration indicates that on-line modifications were being made. It is suggested that a fast route from the retina to the superior colliculus could account for this behaviour and that models of oculomotor control need to be updated.

On-line Emotion Recognition in a 3-D Activation-Valence-Time Continuum using Acoustic and Linguistic Cues

For many applications of emotion recognition, such as virtual agents, the system must select responses while the user is speaking. This requires reliable on-line recognition of the user’s affect. However most emotion recognition systems are based on turnwise processing. We present a novel approach to on-line emotion recognition from speech using Long Short-Term Memory Recurrent Neural Networks. Emotion is recognised frame-wise in a two-dimensional valence-activation continuum. In contrast to current state-of-the-art approaches, recognition is performed on low-level signal frames, similar to those used for speech recognition. No statistical functionals are applied to low-level feature contours. Framing at a higher level is therefore unnecessary and regression outputs can be produced in real-time for every low-level input frame. We also investigate the benefits of including linguistic features on the signal frame level obtained by a keyword spotter.