00000ctm a22000004a 4500 UP-99796217603116287 Buklod 20080209232849.0 m |o d | ta 070806s2006 xx r |||| u|eng d (iLib)UPD-00000179728 DENGII eng LG 993.5 2006 E64 C48 Chua, Ivy Jhoanna A. Automatic isolated word recognition system and phoneme-level segmentation of the Filipino speech corpus using the hidden Markov model toolkit Ivy Jhoanna A. Chua, Nino D.J. Eustaquio. 2006. vii, 92 leaves ill. + 1 computer laser optical disc. Thesis (B.S. Electronics and Communications Engineering)--University of the Philippines, Diliman. Automatic speech recognition systems (ASR) are widespread in today's modern world and have proven its use in many applications. A prerequisite in the development of such systems are segmented and transcribed speech corpus which, when done manually, is a tedious work. Hence, the objective of this project is to develop a system that will automatically recognize speech signals at the word-level, and segment the words of the Filipino Speech Corpus (FSC) word sub-corpus into phoneme-level. The Hidden Markov Model Toolkit (HTK) developed at Cambridge University Engineering Department (CUED) will be used for this purpose. To create HMMs, Mel Frequency Cepstral Coefficients (MFCC) is extracted using the tools provided by HTK, and in turn will be used as input feature vectors. The system will be trained using these feature vectors to recognize words in word level. From the results of the word-level recognition, a phoneme-level recognition system will be developed which will be used for the phoneme segmentation process. The output of this project is an acceptable word recognition rate of the ASR system and the phoneme-level segmentation of the FSC word sub-corpus. Word recognition. Phonemics. Automatic speech recognition. Markov processes. Word recognition system. Filipino speech corpus. Markov Model. Eustaquio, Nino D.J. FI UP UPD DENG-II LG 993.5 2006 E64 C48 Thesis