WaDeNet: Wavelet Decomposition based CNN for Speech Processing

TL;DR

WaDeNet is an end-to-end wavelet-based CNN model for speech processing that integrates spectral feature learning, outperforming current models in mobile health applications with higher accuracy and lower complexity.

Contribution

It introduces WaDeNet, a novel wavelet decomposition integrated CNN architecture for end-to-end speech processing, reducing complexity and improving accuracy in health-related tasks.

Findings

WaDeNet outperforms existing models with a 6.36% accuracy increase.

WaDeNet is lighter than comparable CNN architectures.

Effective for non-invasive emotion recognition in mobile health applications.

Abstract

Existing speech processing systems consist of different modules, individually optimized for a specific task such as acoustic modelling or feature extraction. In addition to not assuring optimality of the system, the disjoint nature of current speech processing systems make them unsuitable for ubiquitous health applications. We propose WaDeNet, an end-to-end model for mobile speech processing. In order to incorporate spectral features, WaDeNet embeds wavelet decomposition of the speech signal within the architecture. This allows WaDeNet to learn from spectral features in an end-to-end manner, thus alleviating the need for feature extraction and successive modules that are currently present in speech processing systems. WaDeNet outperforms the current state of the art in datasets that involve speech for mobile health applications such as non-invasive emotion recognition. WaDeNet achieves an average increase in accuracy of 6.36% when compared to the existing state of the art models. Additionally, WaDeNet is considerably lighter than a simple CNNs with a similar architecture.