Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/SiPS.2012.23 | SiPS |
Keywords | DocType | ISSN |
gaussian mixture model,speech processing,gmm,frequency shift,frequency shift present,super vector,svm,mel-frequency-cepstral-coefficient,frequency shift detection,speech frequency shift detection,gaussian processes,frequency domain,corresponding gmm super vector,spectral shift,energy variation,proposed solution,mfcc,support vector machines,lfcc,gaussian mixture model super vector,certain situation | Conference | 2162-3562 |
ISBN | Citations | PageRank |
978-1-4673-2986-6 | 2 | 0.38 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hua Xing | 1 | 2 | 0.38 |
| Philipos C. Loizou | 2 | 991 | 71.00 |