Efficient CEPSTRAL Normalization for Robust Speech Recognition

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-ind...

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Bibliographic Details
Main Authors: Liu, Fu-Hua, Stern, Richard M, Huang, Xuedong, Acero, Alejandro
Format: Report
Language:English
Subjects:
ACCURACY
ACOUSTICAL ENVIRONMENT
Acoustics
ALGORITHMS
ARRAYS
CEPSTRUM BASED PROCEDURES
ENVIRONMENTS
FLOORS
GAGES
HEARING
INDUSTRIAL PLANTS
LANGUAGE
MICROPHONES
OFFICE BUILDINGS
PASSENGER VEHICLES
QUIET
RECOGNITION
SECONDARY
SPEECH
SPEECH RECOGNITION
TELEPHONE SYSTEMS
Voice Communications
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Summary: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.