Custom-designed SVM kernels for improved robustness of phoneme classification
Custom-designed SVM kernels for improved robustness of phoneme classification
复制标题
定制设计的 SVM 内核可提高音素分类的稳健性
DOI：
--
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Peter Sollich
中科院分区：
文献类型：
--
作者：
J. Yousafzai;Z. Cvetković;Peter Sollich
研究了基于支持向量机的音素分类算法在声学波形域中对白色高斯噪声和粉红噪声的鲁棒性。我们专注于设计内核的语音的物理特性进行调整的问题。为了比较，结果报告的PLP表示的语音使用标准内核。我们表明，主要的改进，可以通过将语音的属性内核。此外，高维声波波形对加性噪声表现出更鲁棒的行为。最后，我们研究了PLP和声学波形表示的组合，该组合在噪声水平的范围内比任何一个单独的表示都能获得更好的分类。
The robustness of phoneme classification to white Gaussian noise and pink noise in the acoustic waveform domain is investigated using support vector machines. We focus on the problem of designing kernels which are tuned to the physical properties of speech. For comparison, results are reported for the PLP representation of speech using standard kernels. We show that major improvements can be achieved by incorporating the properties of speech into kernels. Furthermore, the high-dimensional acoustic waveforms exhibit more robust behavior to additive noise. Finally, we investigate a combination of the PLP and acoustic waveform representations which attains better classification than either of the individual representations over a range of noise levels.