Real-Time Speech Emotion Recognition Using a Pre-trained Image Classification Network: Effects of Bandwidth Reduction and Companding

Abstract

This paper examines the effects of reduced speech bandwidth and the μ-low companding procedure used in transmission systems on the accuracy of speech emotion recognition (SER). A step by step description of a real-time speech emotion recognition implementation using a pre-trained image classification network AlexNet is given. The results showed that the baseline approach achieved an average accuracy of 82% when trained on the Berlin Emotional Speech (EMO-DB) data with seven categorical emotions. Reduction of the sampling frequency from the baseline 16–8 kHz (i.e., bandwidth reduction from 8 to 4 kHz, respectively) led to a decrease of SER accuracy by about 3.3%. The companding procedure on its own reduced the average accuracy by 3.8%, and the combined effect of companding and band reduction decreased the accuracy by about 7% compared to the baseline results. The SER was implemented in real-time with emotional labels generated every 1.033–1.026 s. Real-time implementation timelines are presented.