Towards feature-based speech metric

A speech metric which directly uses spectral features such as spectral peak frequencies and bandwidths is proposed and evaluated. The spectral features either are derived directly by solving the all-pole model polynomial to get spectral peak frequencies and bandwidths and fitting the linear regression line to the logarithmic spectrum of the model or are estimated as a linear combination of the several lower cepstral coefficients of the all-pole model spectrum. The performance of the studied metric in speaker-independent speech recognition of telephone-quality speech approaches the performance of the best weighted cepstral metrics