A Low footprint Automatic Speech Recognition System for Resource Constrained Edge Devices

Abstract

Deep Learning (DL) has been instrumental in pushing artificial intelligence (AI)/ machine learning (ML) algorithms to edge of the network. It allows building AI/ML algorithms for computer vision, speech processing, and other timeseries analytics tasks with limited domain knowledge. As there is no mechanism to control the representations learned from a large dataset, it becomes hard to predict whether a very small DL model can learn the proper dependencies needed for a particular problem at hand. With speech recognition capability becoming important in several Internet of Things (IoT) devices, we propose an explainable AI-based methodology to build small DL models for speech recognition by controlling the representations learned by a model under a hard size constraint. We enhance the architecture of a state of the art sequence transduction model to allow the tuning of accuracy vs. model size trade-off. Using these techniques we achieve a reduction in model size and latency by a factor of 10 and 6 respectively, with only 4loss compared to the embedded implementation of a well known ASR.