Implementation of ANN based speech recognition system on an embedded board

Speech recognition systems are ubiquitous and find its application in automated voice control, voice dialling and automated directory assistance. This paper aims at implementing a neural network based isolated spoken word recognition system on an embedded board — Raspberry Pi using open source software called octave. Mel-Frequency Cepstral Coefficient (MFCC) features are extracted from speech signal and given as input to the neural network. The Feed Forward Multi-Layer Perceptron Neural Network trained with back propagation rule is implemented using Octave in Raspberry Pi. TIDIGITS corpus is used for the experiment. Speaker dependent speech recognition results in 100% accuracy but the speaker independent recognition system shows less accuracy.