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The use of Convolutional Neural Networks (CNN) pre-trained for a particular task, as a feature extractor for an alternate task, is a standard practice in many image classification paradigms. However, to date there have been comparatively few works exploring this technique for speech classification tasks. Herein, we utilise a pretrained end-to-end Automatic Speech Recognition CNN as a feature extractor for the task of food-type recognition from speech. Furthermore, we also explore the benefits of Compact Bilinear Pooling for combining multiple feature representations extracted from the CNN. Key results presented indicate the suitability of this approach. When combined with a Recurrent Neural Network classifier, our strongest system achieves, for a seven-class food-type classification task an unweighted average recall of 73.3 % on the test set of the iHEARu-EAT database.

We introduce the MuSe-Toolbox -a Python-based open-source toolkit for creating a variety of continuous and discrete emotion gold standards. In a single framework, we unify a wide range of fusion methods and propose the novel Rater Aligned Annotation Weighting (RAAW ), which aligns the annotations in a translationinvariant way before weighting and fusing them based on the interrater agreements between the annotations. Furthermore, discrete categories tend to be easier for humans to interpret than continuous signals. With this in mind, the MuSe-Toolbox provides the functionality to run exhaustive searches for meaningful class clusters in the continuous gold standards. To our knowledge, this is the first toolkit that provides a wide selection of state-of-the-art emotional gold standard methods and their transformation to discrete classes. Experimental results indicate that MuSe-Toolbox can provide promising and novel class formations which can be better predicted than hard-coded classes boundaries with minimal human intervention. The implementation 1 is out-of-the-box available with all dependencies using a Docker container 2 . CCS CONCEPTS• Information systems → Multimedia and multimodal retrieval; • Computing methodologies → Artificial intelligence.

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