The Critical Role and Systematic Evaluation of Data Preprocessing in Deep Learning for Speech Emotion Recognition

Abstract

Speech Emotion Recognition (SER) has emerged as a vital research domain that aims to imbue machines with the capability to discern human emotional states from vocal cues. The efficacy of deep learning (DL) models in SER is profoundly dependent on the initial data preprocessing stage. This study provides an in-depth exploration of data preprocessing techniques critical for DL-based SER, including noise reduction, signal and feature normalization, diverse acoustic feature extraction methodologies (e.g., Mel-Frequency Cepstral Coefficients (MFCCs), Mel-spectrograms, Chroma features, and standardized sets such as eGeMAPS), and various data augmentation strategies. Furthermore, we propose a comprehensive framework for the systematic evaluation of these preprocessing pipelines. This framework advocates a rigorous, incremental approach to experimentation designed to isolate and quantify the impact of individual and combined preprocessing steps. The objective is to foster the development of evidence-based guidelines and best practices in SER preprocessing, thereby contributing to the creation of more accurate, robust, and generalizable emotion recognition systems for diverse, real-world applications.