Sigma-delta analog to LPC feature converters for portable recognition interfaces

For many recognition systems, the feature extraction unit forms the most computationally intensive and power consuming component. In this paper, we present a design of an analog-to-information converter that directly produces a pulse-encoded representation of linear predictive coded (LPC) features corresponding to an input analog signal. At the core of proposed design is a sigma-delta modulation procedure that is embedded within a learning step. Measured results from a fabricated prototype in a 0.5 mum CMOS technology demonstrate the real-time functionality of the learner in extracting 6-dimensional online LPC features from input speech signal while consuming only 450 muW.