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Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models
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DOI：10.3390/s21041249.png)
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Abstract
En 中文
The advancements in neural networks and the on-demand need for accurate and near real-time Speech Emotion Recognition (SER) in human-computer interactions make it mandatory to compare available methods and databases in SER to achieve feasible solutions and a firmer understanding of this open-ended problem. The current study reviews deep learning approaches for SER with available datasets, followed by conventional machine learning techniques for speech emotion recognition. Ultimately, we present a multi-aspect comparison between practical neural network approaches in speech emotion recognition. The goal of this study is to provide a survey of the field of discrete speech emotion recognition.
Keywords:
attention mechanism
autoencoders
CNN
deep learning
emotional speech database
GAN
LSTM
machine learning
speech emotion recognition
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