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首页> 外文期刊>IEEE transactions on audio, speech and language processing >Exploiting Acoustic and Syntactic Features for Automatic Prosody Labeling in a Maximum Entropy Framework
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Exploiting Acoustic and Syntactic Features for Automatic Prosody Labeling in a Maximum Entropy Framework

机译：在最大熵框架中利用声音和句法特征进行自动韵律标记

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摘要

In this paper, we describe a maximum entropy-based automatic prosody labeling framework that exploits both language and speech information. We apply the proposed framework to both prominence and phrase structure detection within the Tones and Break Indices (ToBI) annotation scheme. Our framework utilizes novel syntactic features in the form of supertags and a quantized acoustic–prosodic feature representation that is similar to linear parameterizations of the prosodic contour. The proposed model is trained discriminatively and is robust in the selection of appropriate features for the task of prosody detection. The proposed maximum entropy acoustic–syntactic model achieves pitch accent and boundary tone detection accuracies of 86.0% and 93.1% on the Boston University Radio News corpus, and, 79.8% and 90.3% on the Boston Directions corpus. The phrase structure detection through prosodic break index labeling provides accuracies of 84% and 87% on the two corpora, respectively. The reported results are significantly better than previously reported results and demonstrate the strength of maximum entropy model in jointly modeling simple lexical, syntactic, and acoustic features for automatic prosody labeling.
机译：在本文中，我们描述了一种基于最大熵的自动韵律标记框架，该框架可同时利用语言和语音信息。我们将提出的框架应用于声调和断裂指数（ToBI）注释方案内的突出和短语结构检测。我们的框架利用了超级标签形式的新颖句法特征和量化的声韵特征特征，类似于韵律轮廓的线性参数化。所提出的模型是有区别地训练的，并且在为韵律检测任务选择合适的特征方面具有鲁棒性。提出的最大熵声学-句法模型在波士顿大学广播新闻语料库中达到了音高重音和边界音检测准确度，在波士顿方向语料库中达到了79.8％和90.3％。通过韵律中断索引标记进行的短语结构检测在两个语料库上分别提供了84％和87％的准确性。报告的结果明显优于以前报告的结果，并证明了最大熵模型在联合建模简单的词法，句法和声学特征以进行自动韵律标注方面的优势。

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