Deep Learning Techniques for Speech Emotion Recognition: A Review

Sandeep Pandey · Hanumant Singh Shekhawat · S. R. Mahadeva Prasanna

106 citations0/0 files storedInternational Conference Radioelektronika

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

Abstract

This paper presents an introduction to various deep learning techniques with the aim of capturing and classifying emotional state from speech utterances. Architectures such as Convolutional Neural Network(CNN) and Long Short-Term Memory(LSTM) have been used to test the emotion capturing capability from various standard speech represenations such as mel spectrogram, magnitude spectrogram and Mel-Frequency Cepstral Coefficients (MFCC's) on two popular datasets- EMO-DB and IEMOCAP. Experimental findings along with reasoning have been presented as to which architecture and feature combination is better suited for the purpose of speech emotion recognition. This work explores the widely used basic deep learning architectures used in literature.

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2008IEMOCAP: interactive emotional dyadic motion capture database 2008A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions 2005A database of German emotional speech 2010Survey on speech emotion recognition: Features, classification schemes, and databases 2018Speech emotion recognition using deep 1D & 2D CNN LSTM networks 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 2012Emotion recognition from speech: a review

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