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The Speech Emotion Recognition Adaptation Benchmark (SERAB) is a new framework to evaluate the performance and generalization capacity of different approaches for utterance-level SER. The benchmark is composed of nine datasets for SER in six languages. We used the proposed framework to evaluate a selection of standard hand-crafted feature sets and state-of-the-art DNN representations. The results highlight that using only a subset of the data included in SERAB can result in biased evaluation, while compliance with the proposed protocol can circumvent this issue.

Convolutional Neural Networks have been extensively used for solving many vision problems. However, due to high memory and computational requirements, deployment of these models on edge devices is limited. Many embedded friendly models such as MobileNet, ShuffleNet, SqueezeNet, and many more are proposed to serve this purpose. But these models are still not compact enough to deploy on edge devices. The popular metric-based pruning methods (which are aimed at pruning insignificant and redundant filters) could achieve limited compression for embedded friendly models such as MobileNet.

Fetal well-being during labor is currently assessed by medical professionals through visual interpretation of the cardiotocogram (CTG), a simultaneous recording of Fetal Heart Rate (FHR) and Uterine Contractions (UC). This method is disputed due to high inter- and intra-observer variability and a resulting increase in the number of unnecessary interventions. A method for computerized interpretation of the CTG, based on Contrastive Predictive Coding (CPC) is presented here.

Acoustic environments affect acoustic characteristics of sound to be recognized by physically interacting with sound wave propagation. Thus, training acoustic models for audio and speech tasks requires regularization on various acoustic environments in order to achieve robust performance in real life applications. We propose FilterAugment, a data augmen-tation method for regularization of acoustic models on vari-ous acoustic environments.

The notion of a Moreau envelope is central to the analysis of first-order optimization algorithms for machine learning and signal processing. We define a compositional calculus adapted to Moreau envelopes and show how to apply it to deep networks, and, more broadly, to learning systems equipped with automatic differentiation and implemented in the spirit of differentiable programming.

There has been a rising interest in graph neural networks (GNNs) for representation learning over the past few years. GNNs provide a general and efficient framework to learn from graph-structured data. However, GNNs typically only use the information of a very limited neighborhood for each node to avoid over-smoothing. A larger neighborhood would be desirable to provide the model with more information.