Acoustical and Environmental Robustness in Automatic Speech Recognition
Acoustical and Environmental Robustness in Automatic Speech Recognition
Reduced channel dependence for speech recognition
HLT '91 Proceedings of the workshop on Speech and Natural Language
Comparative experiments on large vocabulary speech recognition
HLT '93 Proceedings of the workshop on Human Language Technology
An overview of the SPHINX-II speech recognition system
HLT '93 Proceedings of the workshop on Human Language Technology
Benchmark tests for the DARPA Spoken Language Program
HLT '93 Proceedings of the workshop on Human Language Technology
Comparative experiments on large vocabulary speech recognition
HLT '93 Proceedings of the workshop on Human Language Technology
Signal processing for robust speech recognition
HLT '94 Proceedings of the workshop on Human Language Technology
Channel effect compensation in LSF domain
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
IEICE - Transactions on Information and Systems
Proceedings of the 3rd International Universal Communication Symposium
Word recognition with a hierarchical neural network
NOLISP'07 Proceedings of the 2007 international conference on Advances in nonlinear speech processing
Optimizing acoustic features for source cell-phone recognition using speech signals
Proceedings of the first ACM workshop on Information hiding and multimedia security
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In this paper we describe and compare the performance of a series of cepstrum-based procedures that enable the CMU SPHINX-II speech recognition system to maintain a high level of recognition accuracy over a wide variety of acoustical environments. We describe the MFCDCN algorithm, an environment-independent extension of the efficient SDCN and FCDCN algorithms developed previously. We compare the performance of these algorithms with the very simple RASTA and cepstral mean normalization procedures, describing the performance of these algorithms in the context of the 1992 DARPA CSR evaluation using secondary microphones, and in the DARPA stress-test evaluation.