PERBANDINGAN EKSTRAKSI CIRI FULL SPECTROGRAM IMAGE, BLOCKS SPECTROGRAM IMAGE, DAN ROW MEAN SPECTROGRAM IMAGE DALAM MENGIDENTIFIKASI PEMBICARA; COMPARISON OF EXTRACTION FEATURE FULL SPECTROGRAM IMAGE, BLOCKS SPECTROGRAM IMAGE, AND ROW MEAN SPECTROGRAM IMAGE FOR SPEAKER IDENTIFICATION

LA ODE HASNUDDIN S. SAGALA, Agus Harjoko

2013 | Disertasi | PROGRAM STUDI S2 ILMU KOMPUTER

On a speaker identification system, selection extraction feature methods and feature size are used affect the accuracy of identification. In that regard, this study will presents comparison three extraction feature CBIR methods namely full image, blocks image, and row mean image. The third methods is used for identify the speaker with emphasis on the selection feature vector are used. Sound data obtained from and recorded used mobile phone voice recording. Sound recordings are from 10 speakers with details of 5 men and 5 women. Every speakers pronounce the five sentences namely Selamat Pagi, Selamat Siang, Selamat Sore, Selamat Malam, and Dengan siapa as well as each sentence was repeated eight times. Before the applications of CBIR methods, sound recordings used converted into spectrogram image using STFT. Spectrogram are formed then forwarded to in transform kekre for extracted feature. Use kekre transform aims to select and take the possibilities optimal characteristics also alleviate the computing process. Using reference data 250 spectrogram image and testing data 150 spectrogram image provides results that the full image feature extraction methods obtain a higher percentage of identification is 93.3% with a feature size of 32x32

Kata Kunci : Speaker Identification; Spectrogram; Kekre Transform; Full Image; Blocks Image; Row Mean Image