Title :
Frequency Shift Detection of Speech with GMMs AND SVMs
Author :
Hua Xing ; Loizou, Philipos C.
Author_Institution :
Dept. of Electr. Eng., Univ. of Texas at Dallas, Richardson, TX, USA
Abstract :
In certain situations, speech might be shifted in the frequency domain amid the presence of noise. To be able to compensate for the spectral shift, it is important to know the amount of frequency shift present. A method based on Mel-frequency-cepstral-coefficient (MFCC) and Gaussian Mixture model (GMM) super vector is proposed for detecting frequency shifts in speech. MFCC or LFCC is extracted to characterize the energy variation of the signal. A GMM is trained for each shifted utterance, and the corresponding GMM super vector is used as the input feature for SVM. Results show that the proposed solution could yield good performance.
Keywords :
Gaussian processes; speech processing; support vector machines; Gaussian Mixture model super vector; Mel-frequency-cepstral-coefficient; SVM; frequency domain; spectral shift; speech frequency shift detection; Accuracy; Feature extraction; Filter banks; Mel frequency cepstral coefficient; Speech; Support vector machines; Training; GMM; LFCC; MFCC; SVM; frequency shift;
Conference_Titel :
Signal Processing Systems (SiPS), 2012 IEEE Workshop on
Conference_Location :
Quebec City, QC
Print_ISBN :
978-1-4673-2986-6
DOI :
10.1109/SiPS.2012.23
