SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion Recognition

TL;DR

SigWavNet introduces an end-to-end deep learning framework that combines wavelet transforms with neural networks to improve speech emotion recognition by capturing multi-resolution features directly from raw speech signals.

Contribution

The paper presents a novel wavelet-based deep learning model that learns wavelet bases and denoising jointly, enhancing feature extraction for speech emotion recognition without pre-processing.

Findings

Outperforms state-of-the-art on IEMOCAP and EMO-DB datasets.

Effectively handles variable-length speech signals.

Eliminates need for pre or post-processing steps.

Abstract

In the field of human-computer interaction and psychological assessment, speech emotion recognition (SER) plays an important role in deciphering emotional states from speech signals. Despite advancements, challenges persist due to system complexity, feature distinctiveness issues, and noise interference. This paper introduces a new end-to-end (E2E) deep learning multi-resolution framework for SER, addressing these limitations by extracting meaningful representations directly from raw waveform speech signals. By leveraging the properties of the fast discrete wavelet transform (FDWT), including the cascade algorithm, conjugate quadrature filter, and coefficient denoising, our approach introduces a learnable model for both wavelet bases and denoising through deep learning techniques. The framework incorporates an activation function for learnable asymmetric hard thresholding of wavelet coefficients. Our approach exploits the capabilities of wavelets for effective localization in both time and frequency domains. We then combine one-dimensional dilated convolutional neural networks (1D dilated CNN) with a spatial attention layer and bidirectional gated recurrent units (Bi-GRU) with a temporal attention layer to efficiently capture the nuanced spatial and temporal characteristics of emotional features. By handling variable-length speech without segmentation and eliminating the need for pre or post-processing, the proposed model outperformed state-of-the-art methods on IEMOCAP and EMO-DB datasets. The source code of this paper is shared on the Github repository: https://github.com/alaaNfissi/SigWavNet-Learning-Multiresolution-Signal-Wavelet-Network-for-Speech-Emotion-Recognition.