An ultra low power, ultra miniature voice command system based on Hidden Markov Models

A real-time HMM-based isolated word recognition system is implemented on an ultra low-power miniature DSP system. The DSP system consumes less than 1 milliWatt, much less than what is considered today as “low-resource”. It has a very small footprint and requires only a single hearing aid sized 1 volt battery. The efficient implementation of HMM and MFCC feature extraction algorithms is accomplished through the use of three processing units running concurrently. In addition to the DSP core, an input/output processor creates frames of input speech signals, and a WOLA fi1terbank unit performs windowing, FFT and vector multiplications. A system evaluation using a vocabulary of 18 words shows a success rate of more than 99%.