基于层次模型的集成机器学习的普通话音高重音预测

Chongjia Ni, Wenju Liu, Bo Xu
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引用次数: 4

摘要

在本研究中,我们将普通话特征与普通话声学属性和文本信息结合起来,使用基于层次模型的集成机器学习来预测普通话音高重音。我们的模型可以充分利用韵律层次结构和集成机器学习的优势。将我们的模型与分类回归树(CART)、支持向量机(SVM)、adaboost与CART在不同的实验条件下进行比较,层次模型得到了最好的结果,它对普通话读语音的准确率达到84.75%。同时,在相同的训练集和测试集上,我们将所提出的方法与之前提出的方法进行了比较。绝对准确率分别提高2.25%和0.82%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mandarin pitch accent prediction using hierarchical model based ensemble machine learning
In this study, we combine the Mandarin characteristics with Mandarin acoustic attribute and text information and use hierarchical model based ensemble machine learning to predict Mandarin pitch accent. Our model could make the best of advantages of prosody hierarchical structure and ensemble machine learning. When comparing our model with classification and regression tree (CART), support vector machine (SVM), adaboost with CART at different experimental conditions, the hierarchical model obtains the best results, it can achieve 84.75% accuracy rate to Mandarin read speech. At the same time, we compare our proposed method with previous proposed method at the same training set and test set. There are 2.25% and 0.82% absolute accuracy rate improvements.
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