Spectral Estimation of Voiced Speech using a Family of MVDR Estimates

Abstract

We present a robust approach to modeling voiced speech using a family of minimum variance distortionless response (MVDR) spectral estimates. The method exploits the fact that for a fixed model order, for a sinusoidal signal in noise, the MVDR estimate at the sinusoidal frequency is approximately related to the sinusoidal and noise power in a simple linear manner with the coefficients being dependent on the model order. Modeling voiced speech as a sum of harmonic signals, we then use the aforementioned relationship along with a least squares approach to combine a family of MVDR estimates (MVDR estimates of different orders) and develop a robust approach for modeling voiced speech. Experimental results of spectral estimation of sinusoids, synthetic vowels, and actual speech signals at SNR of 0 dB and 5 dB using this approach indicate an increased resolution in the estimated MVDR spectra. The MFCC computed from the MVDR spectra using this approach are also used for speaker identification experiments on the TEMIT database at various SNR. The results indicate a reasonable improvement in recognition performance when compared to the MFCC and the fixed order MVDR-MFCC.