Implementing a high accuracy continuous speech recognizer on a fixed-point processor

Abstract

A small vocabulary speech recognizer suitable for implementation on a 16-bit fixed-point DSP is described. The input speech xt is sampled at analog-to-digital (A/D) converterand the digital samples are applied to MFCC (Mel-scaled cepstrum coefficients) front end processing. For robustness to background noises, PMC (parallel model combination)is integrated. The MFCC and Gaussian mean vectors are applied to PMC. The MFCC and PMC provide speech features extracted in noise and this is used to modify the HMMs. The noise adapted HMMs excluding mean vectors are applied to the search procedure to recognize the grammar. A method of computing MFCC comprises the steps of: performing dynamic Q-point computation for the preemphasis, Hamming Window, FFT, complex FFT to power spectrum and Mel scale power spectrum into filter bank steps, a log filter bank step and after the log filter bank step performing fixed Q-point computation. A polynomial fit is used to compute log2 in the log filter bank step. The method of computing PMC comprises the steps of: computing noise MFCC profile, computing cosine transform MFCC into mel-scale filter bank, converting log filter bank into linear filter bank with an exponential wherein to compute exp2 a polynomial fit is used, performing a model combination in the linear filter bank domain; and converting the noise compensated linear filter bank into MFCC by log and inverse cosine transform.