| 王家盛,郭其威,吴松,马建敏.基于特征相对贡献度对加权Mel倒谱的改进[J].声学技术,2021,40(3):408~414 |
| 基于特征相对贡献度对加权Mel倒谱的改进 |
| Improvement of weighted Mel cepstrum based on characteristic relative contribution |
| 投稿时间：2020-03-08 修订日期：2020-07-09 |
| DOI：10.16300/j.cnki.1000-3630.2021.03.018 |
| 中文关键词: 增减分量法 贡献度 梅尔(Mel)倒谱系数 高斯混合-通用背景模型(GMM-UBM) |
| 英文关键词: increment-subtraction component method degree of contribution Mel frequency cepstrum coefficient (MFCC) Gaussian mixture model-universal background model (GMM-UBM) |
| 基金项目:上海航天科技创新基金（SAST2019-021）资助项目。 |
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| 中文摘要: |
| 在声纹识别系统的搭建过程中，提高识别率的一个重要做法是使语音信号中能够提取出的特征尽可能包含更多的说话人个性特征。为了探究特征参数各分量对识别系统性能的影响，文章基于高斯混合-通用背景模型（GaussianMixture Model-Universal Background Model，GMM-UBM）基线系统，研究了在无噪环境中各维特征组合下的识别率，利用增减分量法定量计算出各维特征分量对识别率的相对贡献程度，并根据贡献度的强弱对各维特征分量进行合理加权，得到了贡献度拟合权重系数，将此系数用于改进梅尔倒谱系数（Mel Frequency Cepstrum Coefficient，MFCC）特征参数。仿真结果表明，对特征参数进行贡献度拟合权重系数加权后，声纹识别的正确率得到了提升。 |
| 英文摘要: |
| In the process of constructing the voiceprint recognition system, an important way to improve the recognition rate is to make the features extracted from the speech signal contain as many speaker’s personality characteristics as possible. To explore the characteristic parameters of different orders that affect the performance of recognition system, based on GMM-UBM baseline system in this paper, the recognition rates of the feature combinations of various dimensions in noiseless environment are studied. By means of increment-subtraction component method, the relative contribution degrees of various dimensions of feature parameters to the recognition rate are calculated quantitatively. According to the contribution degrees, various dimensions of feature parameters are weighted reasonably, and the weight coefficients based on fitting contribution are obtained, which can be used to improve the Mel frequency cepstrum coefficient (MFCC). The simulation experimental results show that the accuracy of voice print recognition is increased by feature parameters with improved weight coefficients. |
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