Memristive Computational Architecture of an Echo State Network for Real-Time Speech Emotion Recognition

Echo state networks (ESNs) provide an efficient classification technique for spatiotemporal signals. The feedback connections in the ESN topology enable feature extraction of both spatial and temporal components in time series data. This property has been used in several application domains such as image and video analysis, anomaly detection, andspeech recognition. In this research, a hardware architecture was explored for realizing ESN efficiently in power constrained devices. Specifically, a scalable computational architecture applied to speech-emotion recognition was proposed. Two different topologies were explored, with memristive synapses. The simulation results are promising with a classification accuracy of approximately equals 96% for two distinct emotion statuses. Computational Intelligence for Security and Defense Applications , 26 May 2015, 28 May 2015,