Comparative evaluations of several front-ends for robust speech recognition

Abstract

SPEECH RECOGNITION Doh-Suk Kimy, Jae-Hoon Jeongy, Soo-Young Leey, Rhee M. Kilz yDepartment of Electrical Engineering/ zDivision of Basic Science Korea Advanced Institute of Science and Technology 373-1 Kusong-dong, Yusong-gu, Taejon 305-701, Korea E-mail: dsk@eekaist.kaist.ac.kr ABSTRACT Zero-crossings with peak amplitudes (ZCPA) model motivated by human auditory periphery is simple compared with other auditory models, but powerful speech analysis tool for robust speech recognition in noisy environments. In this paper, improvement in recognition rate of ZCPA model is addressed by incorporating time-derivative features with several di erent time-derivative window lengths. Experimental results show that ZCPA has relatively higher sensitivity to derivative window length than conventional feature extraction algorithms. Also, experimental comparisons with several front-ends including some auditorylike schemes in real-world noisy environments demonstrate the robustness of ZCPA model. ZCPA model shows superior performance compared with other frontends especially in noisy condition corrupted by white Gaussian noise.