| Summary: | 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.
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