基于模糊多类支持向量机的声母识别方法

Application of Consonant Recognition Based on Fuzzy Multi-Class Support Vector Machines

声母识别在构音障碍评估中有重要临床意义,而声母时长短、不平稳,传统方法的识别效果不理想。本文使用小波变换对声母信号进行多尺度分析,提取出新的声母特征向量(DWTMFC-CT),可以更精细刻画相似声母的差别,然后利用模糊多类支持向量机进行声母的识别。为降低模糊支持向量机进行多分类时所带来的计算复杂度,使用两阶段算法。实验结果表明,本文算法不仅提高了模糊支持向量机的训练效率,同时对声母有较好的分类效果。

Consonant recognition has important clinical significance in the assessment of dysarthria,while the consonants are so short and unstable that the recognition results of the traditional methods are ineffective.The algorithm described in this paper extracts a new feature（DWTMFC-CT） of the consonants employing wavelet transformation.And the difference of similar consonants can be described more accurately by the feature.And then the algorithm classifies consonants using a multi-class fuzzy support vector machine（FSVM）.In order to reduce the computation complexity caused by using the standard fuzzy support vector machines for multi-class classification,this paper proposes an algorithm based on two stages.The experimental results show that the proposed algorithm can get better classification results while reducing the training time greatly.