Effect of Data Augmentation, Cross-Validation Methods in Robustness of Explainable Speech Based Emotion Recognition
Year: 2024
Type: article
Abstract: Robust and interpretable speech emotion recognition (SER) is pivotal to handle real time noisy conditions as well as to adapt the language and speaking style variations.In this work, the proposed feature vector is validated using the German database EMoDB for speaker dependent (SD) and speaker independent (SI) approaches.Data augmentation techniques of time shifting, pitch shifting and adding Gaussian noise are employed along with data balancing.The baseline feature set includes spectral features of mel-frequency cepstral coefficients (MFCC) and log Mel spectrogram-based features along with time domain features of zero crossing rate and root mean square energy.Proposed reduced feature set is fusion of baseline features with discrete wavelet transform (DWT) based statistical features selected through ANOVA and reduced chroma features obtained using principal component analysis (PCA).Proposed reduced feature vector is validated by support vector machine (SVM), multilayer perceptron (MLP), and Gradient Boosting (XGBoost) classification with holdout and stratified k-fold (k=5, K=10) cross-validation.In speaker-dependent approach, with 10-fold cross-validation accuracy is improved from 73.07%, 74.94%, 71.02% to 93.18%, 94.07%, 87.15% for SVM, MLP and XGBoost classifier respectively.The contribution of discriminative features of proposed reduced feature set in emotion prediction is explained with the Shapley Additive Explanations module.
Source: Traitement du signal
DOI: 10.18280/ts.410344
Authors: Ashwini S. Shinde, Vaishali V. Patil
Language: English
Topic: Emotion and Mood Recognition
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4400041227