Accession Number : AD1003123

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

Descriptive Note : Conference Paper

Corporate Author : Air Force Research Laboratory/RITB Rome United States

Personal Author(s) : Saleh,Qutaiba ; Merkel,Cory ; Kudithipudi,Dhireesha ; Wysocki,Bryant

Full Text :

Report Date : 28 May 2015

Pagination or Media Count : 6

Abstract : 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.

Descriptors :   artificial neural networks , signal processing , simulations , circuits , speech

Distribution Statement : APPROVED FOR PUBLIC RELEASE