Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisakis model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: Linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers error rates and then to evaluate the information expressed by the classifiers confusion matrices. © Springer Science+Business Media, LLC 2011

Enhanced multiclass SVM with thresholding fusion for speech-based emotion classification

As an essential approach to understanding human interactions, emotion classification is a vital component of behavioral studies as well as being important in the design of context-aware systems. Recent studies have shown that speech contains rich information about emotion, and numerous speech-based emotion classification methods have been proposed. However, the classification performance is still short of what is desired for the algorithms to be used in real systems. We present an emotion classification system using several one-against-all support vector machines with a thresholding fusion mechanism to combine the individual outputs, which provides the functionality to effectively increase the emotion classification accuracy at the expense of rejecting some samples as unclassified. Results show that the proposed system outperforms three state-of-the-art methods and that the thresholding fusion mechanism can effectively improve the emotion classification, which is important for applications that require very high accuracy but do not require that all samples be classified. We evaluate the system performance for several challenging scenarios including speaker-independent tests, tests on noisy speech signals, and tests using non-professional acted recordings, in order to demonstrate the performance of the system and the effectiveness of the thresholding fusion mechanism in real scenarios.Peer ReviewedPreprin

Human Subjects Protection and Technology in Prevention Science: Selected Opportunities and Challenges

Internet-connected devices are changing the way people live, work, and relate to one another. For prevention scientists, technological advances create opportunities to promote the welfare of human subjects and society. The challenge is to obtain the benefits while minimizing risks. In this article, we use the guiding principles for ethical human subjects research and proposed changes to the Common Rule regulations, as a basis for discussing selected opportunities and challenges that new technologies present for prevention science. The benefits of conducting research with new populations, and at new levels of integration into participants’ daily lives, are presented along with five challenges along with technological and other solutions to strengthen the protections that we provide: (1) achieving adequate informed consent with procedures that are acceptable to participants in a digital age; (2) balancing opportunities for rapid development and broad reach, with gaining adequate understanding of population needs; (3) integrating data collection and intervention into participants’ lives while minimizing intrusiveness and fatigue; (4) setting appropriate expectations for responding to safety and suicide concerns; and (5) safeguarding newly available streams of sensitive data. Our goal is to promote collaboration between prevention scientists, institutional review boards, and community members to safely and ethically harness advancing technologies to strengthen impact of prevention science

Techniques for Mimicry and Identity Blending Using Morph Space PCA

We describe a face modelling tool allowing image representation in a high-dimensional morph space, compression to a small number of coefficients using PCA [1], and expression transfer between face models by projection of the source morph description (a parameterisation of complex facial motion) into the target morph space. This technique allows creation of an identity-blended avatar model whose high degree of realism enables diverse applications in visual psychophysics, stimulus generation for perceptual experiments, animation and affective computing. © 2013 Springer-Verlag