End-to-End Speech Emotion Recognition With Gender Information

Ting-Wei Sun

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

Many works have focused on speech emotion recognition algorithms. However, most rely on the proper selection of speech acoustic features. In this paper, we propose a novel emotion recognition algorithm that does not rely on any speech acoustic features and combines speaker gender information. We aim to benefit from the rich information from speech raw data, without any artificial intervention. In general, speech emotion recognition systems require manual selection of appropriate traditional acoustic features as classifier input for emotion recognition. Utilizing deep learning algorithms, and the network automatically select important information from raw speech signal for the classification layer to accomplish emotion recognition. It can prevent the omission of emotion information that cannot be direct mathematically modeled as a speech acoustic characteristic. We also add speaker gender information to the proposed algorithm to further improve recognition accuracy. The proposed algorithm combines a Residual Convolutional Neural Network (R-CNN) and a gender information block. The raw speech data is sent to these two blocks simultaneously. The R-CNN network obtains the necessary emotional information from the speech data and classifies the emotional category. The proposed algorithm is evaluated on three public databases with different language systems. Experimental results show that the proposed algorithm has 5.6%, 7.3%, and 1.5%, respectively accuracy improvements in Mandarin, English, and German compared with existing highest-accuracy algorithms. In order to verify the generalization of the proposed algorithm, we use FAU and eNTERFACE databases, in these two independent databases, the proposed algorithm can also achieve 85.8% and 71.1% accuracy, respectively.

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References

2008IEMOCAP: interactive emotional dyadic motion capture database 2005A database of German emotional speech 2010Survey on speech emotion recognition: Features, classification schemes, and databases 2015The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing 1990Analysis, synthesis, and perception of voice quality variations among female and male talkers 2003Speech emotion recognition using hidden Markov models 2016Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network 2017Automatic speech emotion recognition using recurrent neural networks with local attention

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2022MFF-SAug: Multi feature fusion with spectrogram augmentation of speech emotion recognition using convolution neural network 2022Bringing Emotion Recognition Out of the Lab into Real Life: Recent Advances in Sensors and Machine Learning 2022Attention-Based Multi-Learning Approach for Speech Emotion Recognition With Dilated Convolution

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