Design and optimization of a distributed, embedded speech recognition system

In this paper, we present the design and implementation of a distributed sensor network application for embedded, isolated-word, real-time speech recognition. In our system design, we adopt a parameterized-dataflow-based modeling approach to model the functionalities associated with sensing and processing of acoustic data, and we implement the associated embedded software on an off-the-shelf sensor node platform that is equipped with an acoustic sensor. The topology of the sensor network deployed in this work involves a clustered network hierarchy. A customized time division multiple access protocol is developed to manage the wireless channel. We analyze the distribution of the overall computation workload across the network to improve energy efficiency. In our experiments, we demonstrate the recognition accuracy for our speech recognition system to verify its functionality and utility. We also evaluate improvements in network lifetime to demonstrate the effectiveness of our energy-aware optimization techniques.