  Deep neural speech and audio processing systems have a large number of
trainable parameters, a relatively complex architecture, and require a vast
amount of training data and computational power. These constraints make it more
challenging to integrate such systems into embedded devices and utilise them
for real-time, real-world applications. We tackle these limitations by
introducing DeepSpectrumLite, an open-source, lightweight transfer learning
framework for on-device speech and audio recognition using pre-trained image
convolutional neural networks (CNNs). The framework creates and augments
Mel-spectrogram plots on-the-fly from raw audio signals which are then used to
finetune specific pre-trained CNNs for the target classification task.
Subsequently, the whole pipeline can be run in real-time with a mean inference
lag of 242.0 ms when a DenseNet121 model is used on a consumer-grade Motorola
moto e7 plus smartphone. DeepSpectrumLite operates decentralised, eliminating
the need for data upload for further processing. By obtaining state-of-the-art
results on a set of paralinguistics tasks, we demonstrate the suitability of
the proposed transfer learning approach for embedded audio signal processing,
even when data is scarce. We provide an extensive command-line interface for
users and developers which is comprehensively documented and publicly available
at https://github.com/DeepSpectrum/DeepSpectrumLite.

Sound;Machine Learning;Audio and Speech Processing