Low Power Speaker Identification using Look Up-free Gaussian Mixture Model in CMOS

This work discusses a CMOS-based implementation of low power speaker identification (SI) using Gaussian mixture models (GMMs). Conventionally, GMM-based SI relies on repeated access to log-add look-up table (LUT). With increasing dimensional of speaker models $\mathbf{and}/\mathbf{or}$ number of speakers in the database, accesses to the LUT dominate the overall energy expense for SI. In this work, we discuss piece-wise linear approximations to GMM model that eliminate LUT accesses, thereby limiting model parameter storage in the register files alone, while incurring a minimal accuracy drop. We evaluate our scheme on TIMIT corpus where for text-independent SI and with a two-second test speech, our scheme achieves more than $90\%$ accuracy across test-sets. We discuss the detailed architecture of the control unit, datapath, and key modules in our scheme. Compared to an equivalent design that requires LUT accesses in $\mathrm{off}$ -chip memories, our scheme limits power dissipation for SI to $\sim 600\ \mu \mathbf{W}$ and consumes $4\times$ less energy,