An article with a main author from TCS (Tata). Talks about trying to reduce neural network model sizes while retaining high accuracy for edge devices.A Low footprint Automatic Speech Recognition System For Resource Constrained Edge Devices
ABSTRACTDeep Learning (DL) has been instrumental in pushing artificialintelligence (AI)/ machine learning (ML) algorithms to edge of thenetwork. It allows building AI/ML algorithms for computer vision,speech processing, and other timeseries analytics tasks with limiteddomain knowledge. As there is no mechanism to control the repre-sentations learned from a large dataset, it becomes hard to predictwhether a very small DL model can learn the proper dependenciesneeded for a particular problem at hand.With speech recognition capability becoming important in sev-eral Internet of Things (IoT) devices, we propose an explainableAI-based methodology to build small DL models for speech recog-nition by controlling the representations learned by a model undera hard size constraint.We enhance the architecture of a state of the art sequence trans-duction model to allow the tuning of accuracy vs. model size trade-off. Using these techniques we achieve a reduction in model sizeand latency by a factor of 10 and 6 respectively, with only 4losscompared to the embedded implementation of a well known ASR.
https://www.google.com/url?sa=t&source=web&rct=j&url=https://dl.acm.org/doi/abs/10.1145/3417313.3429385&ved=2ahUKEwic35iM2bHtAhV2zTgGHW_SDoc4ChAWMAB6BAgDEAE&usg=AOvVaw1nVN2sP1_CxRxkUzF78dYl
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