WaDeNet: Wavelet Decomposition based CNN for Speech Processing
This work addresses the need for integrated, lightweight speech processing systems for mobile health applications, offering a novel method but with incremental improvements in a specific domain.
The paper tackles the problem of disjoint modules in speech processing systems by proposing WaDeNet, an end-to-end model that embeds wavelet decomposition to learn spectral features directly, achieving a 6.36% average accuracy increase over state-of-the-art models in mobile health applications like emotion recognition.
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.