Memory-efficient modeling and search techniques for hardware ASR decoders

This paper gives an overview of acoustic modeling and search techniques for low-power embedded ASR decoders. Our design decisions prioritize memory bandwidth, which is the main driver in system power consumption. We evaluate three acoustic modeling approaches Gaussian mixture model (GMM), subspace GMM (SGMM) and deep neural network (DNN) and identify tradeoffs between memory bandwidth and recognition accuracy. We also present an HMM search scheme with WFST compression and caching, predictive beam width control, and a word lattice. Our results apply to embedded system implementations using microcontrollers, DSPs, FPGAs, or ASICs.