Graduate Inst. of Commun. Eng., National Taiwan Univ.Tsai, Shang-NienShang-NienTsaiLIN-SHAN LEE2007-04-192018-07-052007-04-192018-07-052003-12http://ntur.lib.ntu.edu.tw//handle/246246/200704191002819http://ntur.lib.ntu.edu.tw/bitstream/246246/200704191002819/1/01318457.pdfhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-33646523099&doi=10.1109%2fASRU.2003.1318457&partnerID=40&md5=58c16a312128bcb51ecf768efff8dfa8Robustness for speech recognition technologies with respect to adverse environments has been a key issue for real applications. Time-frequency principal components (TFPC) features were shown to be a set of powerful data-driven features under matched circumstances, while histogram equalization (HEQ) was proposed as an efficient feature transformation approach to reduce the mismatch between training and testing conditions. In this paper, it is proposed that TFPC features can be well integrated with HEQ. HEQ generates a well-matched environment, in which TFPC features can be properly utilized. Extensive experiments with respect to the AURORA2 database verified that improved performance in adverse circumstances can be achieved. © 2003 IEEE.application/pdf476318 bytesapplication/pdfen-USGraphic methods; Metadata; Adverse environment; Feature transformations; Histogram equalizations; Principal Components; Real applications; Speech recognition technology; Time frequency; Training and testing; Speech recognitionconference paper10.1109/ASRU.2003.13184572-s2.0-33646523099http://ntur.lib.ntu.edu.tw/bitstream/246246/200704191002819/1/01318457.pdf