Co-training succeeds in Computational Paralinguistics
Zixing Zhang, Jun Deng, Björn Wolfgang Schuller · 2013
Data sparsity is one of the major bottlenecks in the field of Computational Paralinguistics. Partially supervised learning approaches can help leverage this problem without the need of cost-intensive human labelling efforts. We thus investigate the feasibility of cotraining for exemplary paralinguistic speech analysis tasks spanning along the time-continuum: from short-term-related emotion to mid-term-related sleepiness and finally to long-term trait of gender. By dividing the acoustic feature space with two views as independent and sufficient as possible, the semi-supervised learning approach of co-training selects instances with high confidence scores in each view, and agglomerates them along with their predictions into initial training sets per iteration. Our experimental results on official Interspeech Computational Paralinguistics Challenge tasks effectively demonstrate co-training's superiority over the baseline formed by single-view self-training, especially for the short- and medium-term tasks emotion and sleepiness recognition.