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

@article{Cummins2014VariabilityCI,
  title={Variability compensation in small data: Oversampled extraction of i-vectors for the classification of depressed speech},
  author={Nicholas Cummins and Julien Epps and Vidhyasaharan Sethu and Jarek Krajewski},
  journal={2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2014},
  pages={970-974}
}
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… CONTINUE READING
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