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Mohammed Saïd Kasttet, Abdelouahid Lyhyaoui, Douae Zbakh, Adil Aramja and Abderazzek Kachkari
Recently, artificial intelligence and data science have witnessed dramatic progress and rapid growth, especially Automatic Speech Recognition (ASR) technology based on Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs). Consequently, new end-to-...
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Lin Xu, Shanxiu Ma, Zhiyuan Shen, Shiyu Huang and Ying Nan
In order to determine the fatigue state of air traffic controllers from air talk, an algorithm is proposed for discriminating the fatigue state of controllers based on applying multi-speech feature fusion to voice data using a Fuzzy Support Vector Machin...
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Yusuf Brima, Ulf Krumnack, Simone Pika and Gunther Heidemann
Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from unlabeled data. By designing pretext tasks that exploit statistical regularities, SSL models can capture useful representations that are ...
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Suryakant Tyagi and Sándor Szénási
Machine learning and speech emotion recognition are rapidly evolving fields, significantly impacting human-centered computing. Machine learning enables computers to learn from data and make predictions, while speech emotion recognition allows computers t...
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Tobias Zeulner, Gerhard Johann Hagerer, Moritz Müller, Ignacio Vazquez and Peter A. Gloor
Current methods for assessing individual well-being in team collaboration at the workplace often rely on manually collected surveys. This limits continuous real-world data collection and proactive measures to improve team member workplace satisfaction. W...
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Jiahao Fan and Weijun Pan
In recent years, automatic speech recognition (ASR) technology has improved significantly. However, the training process for an ASR model is complex, involving large amounts of data and a large number of algorithms. The task of training a new model for a...
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Hyeon-Kyu Noh and Hong-June Park
A convolutional neural network (CNN) transducer decoder was proposed to reduce the decoding time of an end-to-end automatic speech recognition (ASR) system while maintaining accuracy. The CNN of 177 k parameters and a kernel size of 6 generates the proba...
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Georgios P. Georgiou and Aretousa Giannakou
Investigating heritage language (HL)-contact effects on the dominant language has received limited attention despite its importance in understanding the dynamic interplay between linguistic systems in situations of bilingualism. This study compares the a...
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Varsha S. Lalapura, Veerender Reddy Bhimavarapu, J. Amudha and Hariram Selvamurugan Satheesh
The Recurrent Neural Networks (RNNs) are an essential class of supervised learning algorithms. Complex tasks like speech recognition, machine translation, sentiment classification, weather prediction, etc., are now performed by well-trained RNNs. Local o...
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Mondher Bouazizi, Chuheng Zheng, Siyuan Yang and Tomoaki Ohtsuki
A growing focus among scientists has been on researching the techniques of automatic detection of dementia that can be applied to the speech samples of individuals with dementia. Leveraging the rapid advancements in Deep Learning (DL) and Natural Languag...
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