Human Computer Interaction for Intelligent Systems

© 2022 by the authors; CC BY-NC-ND license

pediatric speech audiometry; hearing tests; conditioned play audiometry; human–computer interaction; mechatronic devices; Internet of Things; cyber-physical systems; system control; augmented reality; mixed reality; Azure cloud; sentiment analysis; opinion classification; lexicon-based approach; hybrid approach; lexicon generation; lexicon labelling; particle swarm optimization; spelling correction; natural language processing; diacritization; error model; context model; Natural Language Processing; deep learning; grammar error detection; word embedding; text-independent speaker verification system; self-attentive pooling; multi-layer aggregation; feature recalibration; deep length normalization; speaker embedding; shortcut connections; convolutional neural networks; ResNet; sentiment analysis; human–robot interaction; dictionary approach; machine learning approach; social robotics; human-robot interaction; mental workload; heart rate variability; machine learning; sentiment level evaluation; handicraft product; 3D handicraft products; smartphone applications; user interaction; user’s attracting attention; question-answering forum; healthcare informatics; recommendation system; word embedding; user study; speech emotion recognition; deep learning; attention mechanism; recurrent neural network; long short-term memory; human–computer interaction; user experience; usability evaluation methods; domain usability; domain-specific languages; graphical user interfaces; virtual reality; art therapy; rehabilitation; neurorehabilitation; neuroplasticity; brain injury; ASR; speech-to-text; edge AI; Wav2Vec; transformers; PyTorch; emotion recognition; dimensional to categorical emotion representation mapping; activation; arousal and valence regression; X-vectors; SVM; n/a