用多个分类器集成提升语音质量评估准确率
Ensemble of classifiers for speech evaluation
- 融合七种语音特征与专家评分构建分类模型
- 集成方法使准确率小幅优于单个分类器
- 适合医疗语音评估场景，可推广至相似任务
本文尝试使用二分类器的集成方法解决医学领域中的语音评估问题。基于发音质量的定量评估与专家评分构建数据集，选取7项量化指标作为特征：动态时间规整距离、闵可夫斯基距离、相关系数、最长公共子序列（LCSS）、真实序列编辑距离（EDR）、带真实惩罚的编辑距离（ERP）和合并分裂距离（MSM）。专家对发音质量的评估作为类别标签：1表示高质量，0表示有畸变。对比了五种分类方法的训练效果：逻辑回归（LR）、支持向量机（SVM）、朴素贝叶斯（NB）、决策树（DT）和K近邻（KNN）。同时展示了通过混合方法构建分类器集成的结果。在所研究的数据集上，使用集成方法相比单独使用二分类器，分类准确率略有提升。
原文摘要 · Abstract (English)
The article describes an attempt to apply an ensemble of binary classifiers to solve the problem of speech assessment in medicine. A dataset was compiled based on quantitative and expert assessments of syllable pronunciation quality. Quantitative assessments of 7 selected metrics were used as features: dynamic time warp distance, Minkowski distance, correlation coefficient, longest common subsequence (LCSS), edit distance of real se-quence (EDR), edit distance with real penalty (ERP), and merge split (MSM). Expert as-sessment of pronunciation quality was used as a class label: class 1 means high-quality speech, class 0 means distorted. A comparison of training results was carried out for five classification methods: logistic regression (LR), support vector machine (SVM), naive Bayes (NB), decision trees (DT), and K-nearest neighbors (KNN). The results of using the mixture method to build an ensemble of classifiers are also presented. The use of an en-semble for the studied data sets allowed us to slightly increase the classification accuracy compared to the use of individual binary classifiers.
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