VoiceHD: Hyperdimensional Computing for Efficient Speech Recognition

Abstract

In this paper, we propose VoiceHD, a novel speech recognition technique based on brain-inspired hyperdimensional(HD) computing. VoiceHD maps preprocessed voice signals in the frequency domain to random hypervectors and combines them to compute a hypervector (as learned patterns) representing each class. During inference, VoiceHD similarly computes a query hypervector; the classification task is done by checking the similarity of the query hypervector with all learned hypervectors and finding a class with the highest similarity. We further extend VoiceHD to VoiceHD+NN that uses a neural network with a single small hidden layer to improve the similarity measures. This neural network is a small block directly operating on the similarity outputs of VoiceHD to slightly improve the classification accuracy. We evaluate efficiency of VoiceHD and VoiceHD+NN compared to a deep neural network with three large hidden layers over Isolet spoken letter dataset. Our benchmarking results on CPU show that VoiceHD and VoiceHD+NN provide 11.9× and 8.5× higher energy efficiency, 5.3× and 4.0× faster testing time, and 4.6× and 2.9× faster training time compared to the deep neural network, while providing marginally better classification accuracy.