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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chao-Yin Hsiao Chin Kun Teng Paohwa Yang Hao Ming Huang |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Mech. Eng., Hsiuping Univ. of Sci. & Technol., Taichung, Taiwan (Paohwa Yang) || Dept. of Mech. & Comput. Aided Eng., FCU, China (Hao Ming Huang) || Dept. of Mech. & Comput. Aided Eng., Feng Chia Univ., Taichung, Taiwan (Chao-Yin Hsiao; Chin Kun Teng) |
| Abstract | In this paper, we propose a method for decomposing speech signals, evaluating the discriminative, and determining the representative vectors of signal sets. At first, we decompose all speech signals with the level four Db4 wavelet decomposition to reconstruct the approximation sub-signals of all four levels, transfer all the speech signals and the sub-signals into the Linear Prediction Codes (LPC), and calculate the Difference vectors between the LPC (DLPC) of the speech signals and that of the approximation sub-signals of different levels. We adopt those LPC and DLPC vectors as the feature vectors, evaluate the discriminative of each set of feature vectors, and adopt the mean vectors of the clusters as the representative vectors. We can use those to set the parameters of the speech recognizer. Although the experimental results are only valid for the speech signals and the wavelet functions used for this experimental study, it should provide valuable references in general applications. |
| Starting Page | 1404 |
| Ending Page | 1409 |
| File Size | 487055 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479928378 |
| DOI | 10.1109/ICCA.2014.6871129 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-06-18 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Speech Vectors Speech recognition Approximation methods Wavelet transforms Covariance matrices Neurons speech recognition wavelet transform feature parameters |
| Content Type | Text |
| Resource Type | Article |
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