PROSPECT features and their application to missing data techniques for robust speech recognition

Missing data theory has been applied to the problem of speech recognition in adverse environments. The resulting systems require acoustic models that are expressed in the spectral rather than in the cepstral domain, which leads to loss of accuracy. Cepstral Missing Data Techniques (CMDT) surmount this disadvantage, but require significantly more computation. In this paper, we study alternatives to the cepstral representation that lead to more efficient MDT systems. The proposed solution, PROSPECT features (Projected Spectra), can be interpreted as a novel speech representation, or as an approximation of the inverse covariance (precision) matrix of the Gaussian distributions modeling the log-spectra.