Robust language and speaker identification using image processing techniques combined with PCA

Spoken language and speaker identification has been attracting the researchers across the globe for past several decades. Language identification shares certain similarities with speaker identification; however they both differ from each other in certain aspects. Both language identification and speaker identification problems have till now been dealt with feature extraction techniques like MFCC, PLP, LPCC etc. In this paper, a new feature extraction technique is proposed. Radon transform (RT) is proposed to be used for feature extraction after obtaining the spectrogram from the speech sample. PCA has been used to achieve dimension reduction and to reduce the computational complexity. The performance of the proposed method has been compared for existing identification techniques in the field of language and speaker identification.