Paper: Efficient Cepstral Normalization For Robust Speech Recognition

ACL ID H93-1014
Title Efficient Cepstral Normalization For Robust Speech Recognition
Venue Human Language Technologies
Session Main Conference
Year 1993
Authors

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 devel- oped 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.