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Detecting emotions directly from a speech signal plays an important role in effective human-computer interactions. Existing speech emotion recognition models require massive computational and storage resources, making them hard to implement concurrently with other machine-interactive tasks in embedded systems. In this paper, we propose an efficient and lightweight fully convolutional neural network (FCNN) for speech emotion recognition in systems with limited hardware resources.

We propose a convolutional neural network (CNN) aided factor graphs assisted by mutual information features estimated by a neural network for seizure detection. Specifically, we use neural mutual information estimation to evaluate the correlation between different electroencephalogram (EEG) channels as features. We then use a 1D-CNN to extract extra features from the EEG signals and use both features to estimate the probability of a seizure event. Finally, learned factor graphs are employed to capture the temporal correlation in the signal.

Event detection is challenging in real-world application since new events continually occur and old events still exist which may result in repeated labeling for old events. There- fore, incremental event detection is essential where a model continuously learns new events and meanwhile prevents per- formance from degrading on old events.

In this paper, we describe a phonotactic language recognition model that effectively manages long and short n-gram input sequences to learn contextual phonotacticbased vector embeddings. Our approach uses a transformerbased encoder that integrates a sliding window attention to attempt finding discriminative short and long cooccurrences of language dependent n-gram phonetic units. We then evaluate and compare the use of different phoneme recognizers (Brno and Allosaurus) and sub-unit tokenizers to help select the more discriminative n-grams.

Polytopic matrix factorization (PMF) is a recently introduced matrix decomposition method in which the data vectors are modeled as linear transformations of samples from a polytope. The successful recovery of the original factors in the generative PMF model is conditioned on the "identifiability" of the chosen polytope. In this article, we investigate the problem of determining the identifiability of a polytope. The identifiability condition requires the polytope to be permutation-and/or-sign-only invariant.