PENGENALAN LAFAL BERDASAR EKSTRAKSI CIRI FONEM BAHASA INDONESIA; INDONESIAN PRONOUNCIATION RECOGNITION BASED ON PHONEME EXTRACTION FEATURE

PRIHANDONO, AGUNG, Agus Harjoko

2016 | Disertasi | FMIPA

Speech recognition has been the purpose of many recent research. begining with a simple thought that can a machine understand what human said to it. in this research, i want to propose a combinizing methods to recognize an Indonesian spoken word based on phonemic feature. The system consist of two parts. The first one is constructon of the phonemefeature data, and the second one is recognition module. Part one of the system which is to construct phonemic features from speech data. The speech data are series of indonesian spoken words that has been recorded five time each word. The choosen words determined by a combination of vocal and consonant on one of the syllable of the word. The words data truncated into pieces according to the phonemes that contain in it. After having these piece of phonem data, the next phase is feature extraction. Feature extraction has been obtained using Linear Predictive Coding methods. Before having the final coefficients of the LPC, the first step is preemphasis by digitized the speech signal through a low-order digital filtering. The second step is block the previous signal into frames using hamming window. Instead of using the autocorrelation step each frame of windowed signal is put through the LPC analysis. I admit that the processing of this LPC methods are incomplete. The process stop after obtain the basic LPC coefficients. The order of the LPC is p=10. These coefficients are used as the final features for recognition. The recognition system as the second part of the system consist of proposed methods to detect then recognize phonems and of course LPC methods for extraction the feature. The result of the recognition obtained by mearsuring the Euclidean distance between sample data and testing data. This system has been tested using pieces of all phonemes from training data and tested using 18 new recorded words. The system recognize with precisely while tested using sample data but not with the new recorded spoken word. The success rate for this new data are 7% for threshold of distance measurement 1, reaches 21% for threshold=1,3 and reaches 22% for threshold=1,5.

Kata Kunci : fonem, LPC, digital filtering, frame blocking, euclidean distance