Hybrid generative-discriminative training of Gaussian mixture models

Recent work has shown substantial performance improvements of discriminative probabilistic models over their generative counterparts. However, since discriminative models do not capture the input distribution of the data, their use in missing data scenarios is limited. To utilize the advantages of both paradigms, we present an approach to train Gaussian mixture models (GMMs) in a hybrid generative-discriminative way. This is accomplished by optimizing an objective that trades off between a generative likelihood term and either a discriminative conditional likelihood term or a large margin term using stochastic optimization. Our model substantially improves the performance of classical maximum likelihood optimized GMMs while at the same time allowing for both a consistent treatment of missing features by marginalization, and the use of additional unlabeled data in a semi-supervised setting. For the covariance matrices, we employ a diagonal plus low-rank matrix structure to model important correlations while keeping the number of parameters small. We show that a non-diagonal matrix structure is crucial to achieve good performance and that the proposed structure can be utilized to considerably reduce classification time in case of missing features. The capabilities of our model are demonstrated in extensive experiments on real-world data

Manual versus automated: The challenging routine of infant vocalisation segmentation in home videos to study neuro(mal)development

In recent years, voice activity detection has been a highly researched field, due to its importance as input stage in many real-world applications. Automated detection of vocalisations in the very first year of life is still a stepchild of this field. On our quest defining acoustic parameters in pre-linguistic vocalisations as markers for neuro(mal)development, we are confronted with the challenge of manually segmenting and annotating hours of variable quality home video material for sequences of infant voice/vocalisations. While in total our corpus comprises video footage of typically developing infants and infants with various neurodevelopmental disorders of more than a year running time, only a small proportion has been processed so far. This calls for automated assistance tools for detecting and/or segmenting infant utterances from real-live video recordings. In this paper, we investigated several approaches of infant voice detection and segmentation, including a rule-based voice activity detector, hidden Markov models with Gaussian mixture observation models, support vector machines, and random forests. Results indicate that the applied methods could be well applied in a semi-automated retrieval of infant utterances from highly non-standardised footage. At the same time, our results show that, a fully automated approach for this problem is yet to come