Variance in Classifying Affective State via Electrocardiogram and Photoplethysmography

Advances in wearable technology have significantly increased the sensitivity and accuracy of devices for recording physiological signals. Commercial off-the-shelf wearable devices can gather large quantities of physiological data un-obtrusively. This enables momentary assessments of human physiology, which provide valuable insights into an individual's health and psychological state. Leveraging these insights provides significant benefits for human-to-computer interaction and personalised healthcare. This work contributes an analysis of variance occurring in features representative of affective states extracted from electrocardiograms and photoplethysmography; subsequently identifies the cardiac measures most descriptive of affective states from both signals and provides insights into signal and emotion-specific cardiac measures; finally baseline performance for automated affective state detection from physiological signals is established.

Linguistic and Gender Variation in Speech Emotion Recognition using Spectral Features

This work explores the effect of gender and linguistic-based vocal variations on the accuracy of emotive expression classification. Emotive expressions are considered from the perspective of spectral features in speech (Mel-frequency Cepstral Coefficient, Melspectrogram, Spectral Contrast). Emotions are considered from the perspective of Basic Emotion Theory. A convolutional neural network is utilised to classify emotive expressions in emotive audio datasets in English, German, and Italian. Vocal variations for spectral features assessed by (i) a comparative analysis identifying suitable spectral features, (ii) the classification performance for mono, multi and cross-lingual emotive data and (iii) an empirical evaluation of a machine learning model to assess the effects of gender and linguistic variation on classification accuracy. The results showed that spectral features provide a potential avenue for increasing emotive expression classification. Additionally, the accuracy of emotive expression classification was high within mono and cross-lingual emotive data, but poor in multi-lingual data. Similarly, there were differences in classification accuracy between gender populations. These results demonstrate the importance of accounting for population differences to enable accurate speech emotion recognition.