An analog front-end speech processor using the ratio spectrum
We have combined the standard front-end filter bank with a feature extraction process to produce a model that requires dramatically less hardware (or software). This new model, based on the frequency-sampled ratio spectrum, can be interpreted as a small set of constant-Q filters whose center frequencies adapt to locations of high signal energy, The resulting feature vectors are shown to outperform several competing techniques for phoneme recognition, Results from fabricated CMOS analog VLSI circuits illustrate a hardware efficient method to sample the ratio spectrum.