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    黄平牧 刘 刚 郭 军. 一种新颖的基频包络聚类方法[J]. 计算机研究与发展, 2008, 45(8): 1362-1370.
    引用本文: 黄平牧 刘 刚 郭 军. 一种新颖的基频包络聚类方法[J]. 计算机研究与发展, 2008, 45(8): 1362-1370.
    Huang Pingmu, Liu Gang, and Guo Jun. A Novel Method for Pitch Contour Clustering[J]. Journal of Computer Research and Development, 2008, 45(8): 1362-1370.
    Citation: Huang Pingmu, Liu Gang, and Guo Jun. A Novel Method for Pitch Contour Clustering[J]. Journal of Computer Research and Development, 2008, 45(8): 1362-1370.

    一种新颖的基频包络聚类方法

    A Novel Method for Pitch Contour Clustering

    • 摘要: 主要研究音节基频包络的聚类问题.在聚类的基础上,通过合理的样本选择,可以实现对大语料库的裁减,再结合现有的语音编码技术,就能够构建出一个小存储容量多样本的带调音节语音库,来满足嵌入式TTS系统对合成语音清晰度和自然度的要求.针对音节基频包络长度的不同,给出了一种非定长包络的聚类方法,这种方法将DP(dynamic programming)的概念融入了聚类.首先利用DP的思想,在两个基频包络之间寻找一条最佳路径,然后再沿这一路径进行两包络的相似度计算,若两包络形状类似,距离测度的值会很小.实验表明,与传统的方法相比,使用新方法可以获得更好的聚类结果.合成实验也验证了这种方法的有效性.

       

      Abstract: In this paper, the clustering problem of syllable pitch contours is studied. By doing clustering and reasonable sample selection, the size of the large speech corpus can be significantly reduced. Besides, by introducing the speech coding technique, a small-size multi-sample tonal mono-syllable corpus can be built to satisfy the demands of clarity and naturalness for embedded text-to-speech systems. For pitch contours with different lengths, a non-fixed-length contours clustering approach is proposed. This approach introduces the idea of dynamic programming (DP) into clustering. Firstly, the pitch of contours is normalized (zero-mean). Then, the best path is found between two contours using the DP method. Finally, the distance measure of two contours along this path is calculated. If the shapes of the two pitch contours are similar, the distance measure value will be very low. In the stage of sample selection, the tone domain of syllables is divided by pitch means and then the typical samples are identified according to its levels and clusters. Clustering experiments show that better clustering results can be achieved by this approach compared with traditional approaches. And new clustering approach is also validated by synthesis experiments.

       

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