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- W4312364103 abstract "Automatic 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." @default.
- W4312364103 created "2023-01-04" @default.
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- W4312364103 date "2022-07-18" @default.
- W4312364103 modified "2023-10-17" @default.
- W4312364103 title "Comparing Neural Networks for Speech Emotion Recognition in Customer Service Interactions" @default.
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- W4312364103 doi "https://doi.org/10.1109/ijcnn55064.2022.9892165" @default.
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