Abstract
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.
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