The scripts provided in this software package were written to perform the feature extraction in automatic speech recogniton experiments and to evaluate the obtained recognition performance in [1].

This includes the calculation of:

• Logarithmically scaled Mel-spectrograms (LogMS) in log_mel_spectrogram.m
• Mel-frequency cepstral coefficient (MFCC) features in mfcc_feature_extraction.m
• Gabor filter bank features (GBFB) features in gbfb_feature_extraction.m
• Separable Gabor filter bank (SGBFB) features in sgbfb_feature_extraction.m
• Histogram equalization (HEQ) in heq.m
• Equal-performance SNR increase (EPSI) in epsi.m

Detailed explanations of the corresponding concepts are provided in [1]. The SGBFB feature extraction is closely related to the GBFB feature extraction which was introduced in [2]. For an overview of current publications and further developments of the SGBFB front-end, visit http://medi.uni-oldenburg.de/SGBFB

play_demo.m is demonstrates the use of the feature extraction scripts.

[1] M.R. Schädler and B. Kollmeier, "Separable spectro-temporal Gabor filter bank features: Reducing the complexity of robust features for automatic speech recognition", J. Acoust. Soc. Am. Volume 137, Issue 4, pp. 2047-2059, DOI: 10.1121/1.4916618, URL: http://link.aip.org/link/?JAS/137/2047 (2015)

[2] M.R. Schädler, B.T. Meyer, B. Kollmeier "Spectro-temporal modulation subspace-spanning filter bank features for robust automatic speech recognition", J. Acoust. Soc. Am. Volume 131, Issue 5, pp. 4134-4151, DOI: 10.1121/1.3699200, URL: http://link.aip.org/link/?JAS/131/4134 (2012)