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Studies over het onderwijs in de moderne vreemde talen, deel III: Het omschrijven van gebruik van moderne vreemde talen
No full textStudies over het onderwijs in de moderne vreemde talen, deel II: moderne vreemde talen in examenpakketten A.V.O.
No full textStudies over het onderwijs in de moderne vreemde talen, deel II: moderne vreemde talen in examenpakketten A.V.O.
No full text
Studies over het onderwijs in de moderne vreemde talen, deel 1: Voorstudie over behoeften aan kennis van vreemde talen
No full textStudies over het onderwijs in de moderne vreemde talen, deel 1: Voorstudie over behoeften aan kennis van vreemde talen
No full textComparing Neural Networks for Speech Emotion Recognition in Customer Service Interactions
No full textAutomatic speech emotion recognition (SER) may assist call center service employees in deciphering and regulating customer emotions. In order to contribute to a successful augmentation of service employees with AI, the main goal of this study is to identify effective machine learning approaches to classify discrete basic emotions in customer service conversations. A comparison is presented of the recognition performance of different neural network architectures on speech features extracted from service interactions in a naturalistic customer service setting. Baseline classifiers, including a zerorule classifier, a random classifier, a frequency classifier, and nonsequential multi-class classifiers are compared to different neural network architectures. A multi-layer perceptron (MLP), a one-dimensional convolutional neural network (CNN), and a neural machine translation (NMT) outperform the baseline classifiers, suggesting a pattern in the data relating to emotion labels. While the neural machine translation model with attention attains the highest f1-score, no significant difference in performance among the neural networks is detected. Results therefore support the use of the the multi-label multi-layer perceptron as the simplest model
Comparing Neural Networks for Speech Emotion Recognition in Customer Service Interactions
No full textAutomatic speech emotion recognition (SER) may assist call center service employees in deciphering and regulating customer emotions. In order to contribute to a successful augmentation of service employees with AI, the main goal of this study is to identify effective machine learning approaches to classify discrete basic emotions in customer service conversations. A comparison is presented of the recognition performance of different neural network architectures on speech features extracted from service interactions in a naturalistic customer service setting. Baseline classifiers, including a zerorule classifier, a random classifier, a frequency classifier, and nonsequential multi-class classifiers are compared to different neural network architectures. A multi-layer perceptron (MLP), a one-dimensional convolutional neural network (CNN), and a neural machine translation (NMT) outperform the baseline classifiers, suggesting a pattern in the data relating to emotion labels. While the neural machine translation model with attention attains the highest f1-score, no significant difference in performance among the neural networks is detected. Results therefore support the use of the the multi-label multi-layer perceptron as the simplest model