Abstract
Speech is the most natural form of communication for human beings, and among others, it provides information about the speaker’s emotional state. The current study focuses on automatic speech emotion recognition based on classic and innovated machine learning approaches using simulated emotional speech data. Specifically, individual Gaussian mixture models (GMM) trained for each emotion, a universal background GMM model (UBM-GMM) adapted to each emotion using maximum posteriori (MAP) adaptation, and an approach based on i-vector paradigm, widely used in speaker recognition and language identification, and adapted to emotion recognition are used. When using individual GMMs, a novel technique based on multiple classifiers and late fusion is also applied. In this case, a 90.9% recognition rate is been obtained. When the state-of-the-art, i-vector paradigm based method, along with probabilistic linear discriminant analysis (PLDA) model is used, a 91.4% average rate for speaker-independent Japanese speech emotion recognition is achieved, which is a very promising result and superior to similar studies. In addition to the Japanese emotion recognition, pair-wise recognition for seven emotions in German language has also been conducted. The recognition rates obtained using the German database show the same tendency as in Japanese. In this experiment, an 89.2% average rate has been achieved.
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Heracleous, P., Ishikawa, A., Yasuda, K., Kawashima, H., Sugaya, F., Hashimoto, M. (2018). Machine Learning Approaches for Speech Emotion Recognition: Classic and Novel Advances. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_14
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DOI: https://doi.org/10.1007/978-3-319-77116-8_14
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