شماره ركورد كنفرانس :
2690
عنوان مقاله :
Improving Speech Identification with MFCC and SVM
پديدآورندگان :
vandaki saeed نويسنده , Zahiri Rad Saman نويسنده 1Department of Electrical Engineering, Islamic Azad University, Gonabad branch, Iran. , mehrshad naser نويسنده
كليدواژه :
Mel-frequency cepstral coefficients , Support Vector Machines(SVMs) , (MFCC)
عنوان كنفرانس :
مجموعه مقالات دومين كنفرانس توسعه كاربردهاي صنعتي اطلاعات ارتباطات و محاسبا ت
چكيده فارسي :
In any language, Spoken alphabet identification as one
of the subsets ofspeech identificationand pattern
identification has many applications. However, it is not
easy to recognize the alphabet. Similar sound that is
difficult to detect. One of the problems set is called the
E-set that include words letters B, C, D, E, G, P, T, V
and Z. This paper describes an approach of speech
identification by using the Mel-Scale Frequency
Cepstral Coefficients (MFCC) extracted from speech
signal of spoken words. The Support Vector Machine
(SVM) is used as classifier. In this paper, a method is
said to have achieved %08 accuracy on data-set TI
ALPHA.
شماره مدرك كنفرانس :
3365932
