Name
Abstract | ||
|---|---|---|
This paper presents a method to extract tone relevant features based on pitch flux from continuous speech signal. The autocorrelations of two adjacent frames are calculated and the covariance between them is estimated to extract multi-dimensional pitch flux features. These features, together with MFCCs, are modeled in a 2-stream GMM models, and are tested in a 3-dialect identification task for Chinese. The pitch flux features have shown to be very effective in identifying tonal languages with short speech segments. For the test speech segments of 3 seconds, 2-stream model achieves more than 30% error reduction over MFCC-based model. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICASSP.2006.1660199 | 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, Vols 1-13 |
Keywords | Field | DocType |
speaker recognition,testing,feature extraction,data mining,gaussian processes,speech processing,speech segmentation,autocorrelation,natural languages,speech recognition | Speech processing,Mel-frequency cepstrum,Pattern recognition,Computer science,Feature extraction,Speech recognition,Speaker recognition,Natural language,Gaussian process,Artificial intelligence,Autocorrelation,Covariance | Conference |
ISSN | Citations | PageRank |
1520-6149 | 12 | 1.01 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Ma | 1 | 600 | 47.26 |
| Donglai Zhu | 2 | 117 | 13.59 |
| Rong Tong | 3 | 108 | 11.33 |