Variability compensation in small data: Oversampled extraction of i-vectors for the classification of depressed speech

Variations in the acoustic space due to changes in speaker mental state are potentially overshadowed by variability due to speaker identity and phonetic content. Using the Audio/Visual Emotion Challenge and Workshop 2013 Depression Dataset we explore the suitability of i-vectors for reducing these latter sources of variability for distinguishing between low or high levels of speaker depression. In addition we investigate whether supervised variability compensation methods such as Linear Discriminant Analysis (LDA), and Within Class Covariance Normalisation (WCCN), applied in the i-vector domain, could be used to compensate for speaker and phonetic variability. Classification results show that i-vectors formed using an over-sampling methodology outperform a baseline set by KL-means supervectors. However the effect of these two compensation methods does not appear to improve system accuracy. Visualisations afforded by the t-Distributed Stochastic Neighbour Embedding (t-SNE) technique suggest that despite the application of these techniques, speaker variability is still a strong confounding effect.