A two-stage noise reduction approach for robust speaker-independent speech recognition is presented. The application is speech recognition in the presence of stationary and non-stationary car noise. The two-stage method calculates noise robust NSS-IMELDA feature vectors, which are used as input to Continuous Hidden Markov Models (CHMM). The new NSS-IMELDA features are based on an integration of two noise robust feature extraction methods - the Non-linear Spectral Subtraction (NSS) method  - developed by Lockwood et al. and the Integrated Mel-scale with Linear Discriminant Analysis (IMELDA) method  developed by Hunt et al. The NSS-IMELDA features result in better speech recognition in noise than pure an NSS based - or an IMELDA based recognition system. Results on the test database consisting of the test part of the TI-DIGITS database  and a non-stationary car noise database gave recognition rates of 98.8% and 98.9% at 0 dB and 15 dB, respectively.
Bibliographic reference. Sorensen, Helge B. D. / Hartmann, Uwe (1993): "Robust speaker-independent speech recognition using non-linear spectral subtraction based IMELDA", In EUROSPEECH'93, 235-238.