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倪崇嘉, 张爱英, 刘文举, 徐波. 自动韵律间断检测和特征分析[J]. 计算机科学技术学报, 2012, 27(6): 1184-1196. DOI: 10.1007/s11390-012-1295-z
引用本文: 倪崇嘉, 张爱英, 刘文举, 徐波. 自动韵律间断检测和特征分析[J]. 计算机科学技术学报, 2012, 27(6): 1184-1196. DOI: 10.1007/s11390-012-1295-z
Chong-Jia Ni, Ai-Ying Zhang, Wen-Ju Liu, Bo Xu. Automatic Prosodic Break Detection and Feature Analysis[J]. Journal of Computer Science and Technology, 2012, 27(6): 1184-1196. DOI: 10.1007/s11390-012-1295-z
Citation: Chong-Jia Ni, Ai-Ying Zhang, Wen-Ju Liu, Bo Xu. Automatic Prosodic Break Detection and Feature Analysis[J]. Journal of Computer Science and Technology, 2012, 27(6): 1184-1196. DOI: 10.1007/s11390-012-1295-z

自动韵律间断检测和特征分析

Automatic Prosodic Break Detection and Feature Analysis

  • 摘要: 自动韵律间断检测和标注对语音理解和自然语音合成都十分重要.本文中,我们主要讨论了自动韵律间断检测和特征分析.本文的主要贡献在于以下两个方面:一方面是分类器结合的韵律间断检测.我们利用分类器结合的方法通过利用声学、词典和语法信息检测汉语和英语的韵律间断.我们提出的方法在汉语韵律标注的语料库—ASCCD和英语韵律标注库—Boston University Radio News Corpus (BURNC)都取得了较基线系统和他人要好的实验结果.另一方面是特征分析.我们分析和比较了不同信息源的特征,如时长、基频、能量和音强,在汉语和英语韵律间断检测中的作用,并验证了一些语言学上的结论.

     

    Abstract: Automatic prosodic break detection and annotation are important for both speech understanding and natural speech synthesis. In this paper, we discuss automatic prosodic break detection and feature analysis. The contributions of the paper are two aspects. One is that we use classifier combination method to detect Mandarin and English prosodic break using acoustic, lexical and syntactic evidence. Our proposed method achieves better performance on both the Mandarin prosodic annotation corpus — Annotated Speech Corpus of Chinese Discourse and the English prosodic annotation corpus — Boston University Radio News Corpus when compared with the baseline system and other researches' experimental results. The other is the feature analysis for prosodic break detection. The functions of different features, such as duration, pitch, energy, and intensity, are analyzed and compared in Mandarin and English prosodic break detection. Based on the feature analysis, we also verify some linguistic conclusions.

     

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