Work

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

Year: 2020

Type: article

Abstract: This paper provides a step by step introduction to real-time speech emotion recognition (SER) using a pre-trained image classification network. The procedure has low computational requirements and can be implemented on different voice-based communication platforms such as mobile phones, call centers, and online communication facilities. Effects of reduced speech bandwidth and the mu-low companding procedure used in transmission systems on the SER accuracy are examined. 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 kHz to 8 kHz (i.e., bandwidth reduction from 8 kHz to 4 kHz respectively) led to a decrease of SER accuracy by about 3.3%. The companding procedure reduced the result by 3.8%, and the combined effect of both factors further reduced the average accuracy by about 7% compared to the baseline results. The SER was implemented in real-time with emotional labels generated every 1.033 to 1.026 seconds. Real-time implementation timelines are presented.

Source: Frontiers in Computer Science

DOI: 10.3389/fcomp.2020.00014

Authors: Margaret Lech, Melissa Stolar, Christopher Best, Robert Bolia

Institutions: RMIT University, Defence Science and Technology Group

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,150

Funder: Defence Science and Technology Group