A comparison of subspace feature-domain methods for language recognition

Compensation of cepstral features for mismatch due to dissimilar train and test conditions has been critical for good performance in many speech applications. Mismatch is typically due to variability from changes in speaker, channel, gender, and environment. Common methods for compensation include RASTA, mean and variance normalization, VTLN, and feature warping. Recently, a new class of subspace methods for model compensation have become popular in language and speaker recognition—nuisance attribute projection (NAP) and factor analysis. A feature space version of latent factor analysis has been proposed. In this work, a feature space version of NAP is presented. This new approach, fNAP, is contrasted with feature domain latent factor analysis (fLFA). Both of these methods are applied to a NIST language recognition task. Results show the viability of the new fNAP method. Also, results indicate when the different methods perform best.