A powerful feature extraction system for noise
robust speech recognition was standardized by ETSI.
The system was developed for Distributed Speech
Recognition (DSR) and includes an Advanced Front-
End (AFE) to be implemented in client terminals,
which send the extracted parameters to a remote
server that runs a speech recognition engine. In view
of the integration of a noise-robust front-end in an
embedded speech recognition system, which performs
simultaneously the feature extraction and the speech
recognition tasks, we propose a modified implementation
of the front-end with less computational
requirements. Using the Aurora 2 speech database, we
evaluate the impact on performance of the Blind
Equalization (BE) block, the Gain Factorization (GF)
block and the SNR-dependent Waveform Processing
(SWP) block that are used in the AFE. We conclude
that our modified front-end using Cepstral Mean
Normalization (CMN) and dropping BE, GF and SWP,
outperforms the AFE in a practical task.