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Physical exertion is a stress condition that affects how we normally produce speech. It alters both the temporal and spectral pattern of speech characteristics. Therefore, speech utterances can be used as a cost-effective telehealth solution to detect whether a person is under physical exertion. This paper deals with the detection of shortness of breath (or out-of-breath) condition from speech under physical load by using an multi-task learning (MTL) framework. The primary classification targets are the neutral and the out-of-breath classes. Naturally these are binary targets and do not reflect the actual extent of exertion. This leads to the creation of a novel auxiliary target learning in collaboration with a pre-trained expert system. The targets here indicate the level of exertion under physical load. The MTL framework for both the convolutional neural network (CNN) and CNN-long short-term memory (CLSTM) network performs nearly 1.5% (F1-score) better than the single task learning (STL) framework. In addition to that, out-of-breath speech has more influence on the lower frequency spectrum. Warped spectrograms are given as input to the networks, enabling the deep networks to focus on lower spectral regions. The non-linear frequency warping is achieved by Mel-scale transformation and constant-Q-transform (CQT). CQT, being less dependent on fixed-window size, shows at least 6.57% (F1-score) improvement over Mel-based inputs. The MTL framework, combined with the warped spectrum, performs better in classifying out-of-breath speech from neutral. • Aims to detect speech-based out-of-breath (or physical load) conditions. • Efficiently determines the extent of physical exertion using speech utterances. • Lower frequency-centric approach for better detection of out-of-breath condition. • Multi-task learning further improves the performance by combing the above two steps. [ABSTRACT FROM AUTHOR]

Sexism, a form of oppression based on one's sex, manifests itself in numerous ways and causes enormous suffering. In view of the growing number of experiences of sexism reported online, automatically classifying these recollections can aid in the battle against sexism by allowing gender studies researchers and government officials involved in policymaking to conduct more effective analyses. This paper investigates the 23-class fine-grained, multi-label classification of accounts (reports) of sexism. Moreover, we propose a knowledge-based cascaded multi-task framework for fine-grained multi-label sexism classification. We leverage several supporting tasks, including homogeneous and heterogeneous auxiliary tasks. Homogeneous tasks are set up without incurring any manual labeling cost and heterogeneous tasks are set up which have a high correlation with the accounts of sexism. Unlabeled accounts of sexism are utilized through unsupervised learning to help construct our multi-task setup. In addition, we incorporate a knowledge module within the framework to infuse external knowledge features into the learning process. Further, we investigate transfer learning that employs weakly labeled accounts of sexism and transfers the learning to the multi-label sexism classification. We also devise objective functions that exploit label correlations in the training data explicitly. Multiple proposed multi-task methods outperform the state-of-the-art multi-label sexism classification across five standard metrics. • Propose a knowledge-based cascaded multi-task neural framework for the multi-label sexism classification. • Explore two types of auxiliary tasks: (a) Homogeneous tasks and (b) Heterogeneous tasks • Trained the model with weakly labeled data and transfered the knowledge to the sexism classification task. • Proposed loss functions aimed at tapping label correlations in the multi-label data explicitly. [ABSTRACT FROM AUTHOR]