  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.
