Identification of prosodic phrases based on syntax dependency and conditional random fields
QIAN Yili1,2, ZHANG Ermeng1
1. School of Computer & Information Technology, Shanxi University, Taiyuan 030006, China; 2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 03006, China
Abstract：Synthesized speech quality can be significantly improved by correctly dividing the prosodic structure of sentences. The feature selection is then one of the key factors of prosodic structure prediction. In Chinese information processing, the text features can be divided into shallow text features and deep text features, with the shallow features including words, parts of speech, word length and other factors while the deep features include syntactic information, semantic information and other factors. The relationships between the syntactic dependency structure and the prosodic structure were analyzed to identify the shallow and deep text features in the text with a conditional random field model used for prosodic phrase prediction. This study first uses the shallow text features to recognize the prosodic phrases and then adds the syntactic dependency deep text features to construct the model. Tests show that the accuracy is increased by 13.3%, the recall rate is increased by 14.69%, and the F-score is increased by 14.1%.
钱揖丽, 张二萌. 基于句法依存和条件随机场的韵律短语识别[J]. 清华大学学报（自然科学版）, 2019, 59(7): 530-536.
QIAN Yili, ZHANG Ermeng. Identification of prosodic phrases based on syntax dependency and conditional random fields. Journal of Tsinghua University(Science and Technology), 2019, 59(7): 530-536.
[1] 曹剑芬. 基于语法信息的汉语韵律结构预测[J]. 中文信息学报, 2003, 17(3):41-46.CAO J F. Prediction of prosodic organization based on grammatical information[J]. Journal of Chinese Information Processing, 2003, 17(3):41-46. (in Chinese) [2] 应宏, 蔡莲红. 基于结构助词驱动的韵律短语界定的研究[J].中文信息学报, 1999, 13(6):41-46, 64.YING H, CAI L H. Research on the segmentation of the prosodic phrase based on driven by the structural auxiliary word[J]. Journal of Chinese Information Processing, 1999, 13(6):41-46, 64. (in Chinese) [3] 吴晓如, 王仁华, 刘庆峰. 基于韵律特征和语法信息的韵律边界检测模型[J]. 中文信息学报, 2003, 17(5):48-54.WU X R, WANG R H, LIU Q F. Detection model of prosodic boundary based on prosodic features and syntactic information[J]. Journal of Chinese Information Processing, 2003, 17(5):48-54. (in Chinese) [4] 包森成. 基于统计模型的韵律结构预测研究[D]. 北京:北京邮电大学, 2009.BAO S C. Research on prosodic structure prediction based on statical model[D]. Beijing:Beijing University of Posts and Telecommunications, 2009. (in Chinese) [5] 王琦. 基于深度神经网络的韵律结构预测研究[D]. 北京:北京交通大学, 2016.WANG Q. Research on prosodic structure prediction based on deep neural network[D]. Beijing:Beijing Jiaotong University, 2016. (in Chinese) [6] 冯志茹. 基于语块的汉语韵律短语边界识别研究[D]. 太原:山西大学, 2015.FENG Z R. A research on identification of Chinese prosodic phrase boundary based on Chinese chunk[D]. Taiyuan:Shanxi University, 2015. (in Chinese) [7] 朱玲. 基于句法特征的汉语韵律边界预测的研究[D]. 兰州:西北师范大学, 2013.ZHU L. Research on predicting Chinese prosodic boundary based on syntactic features[D]. Lanzhou:Northwest Normal University, 2013. (in Chinese) [8] DONG Y, ZHOU T, DONG C Y, et al. A two-stage prosodic structure generation strategy for mandarin text-to-speech systems[J]. Acta Automatica Sinica, 2010, 36(11):1569-1574. [9] NAKAMURA C, ARAI M, MAZUKA R. Immediate use of prosody and context in predicting a syntactic structure[J]. Cognition, 2012, 125(2):317-323. [10] 邵艳秋, 穗志方, 韩纪庆, 等. 基于依存句法分析的汉语韵律层级自动预测技术研究[J]. 中文信息学报, 2008, 22(2):116-123.SHAO Y Q, SUI Z F, HAN J Q, et al. A study on Chinese prosodic hierarchy prediction based on dependency grammar analysis[J]. Journal of Chinese Information Processing, 2008, 22(2):116-123. (in Chinese) [11] ROBINSON J J. Dependency structures and transformational rules[J]. Language, 1970, 46(2):259-285. [12] LAFFERTY J D, MCCALLUM A, PEREIRA F C N. Conditional random fields:Probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the 18th International Conference on Machine Learning. San Francisco, USA:Morgan Kaufmann Publishers, 2001:282-289. [13] 吕雁飞, 侯子骄, 张凯. 多分类BP-AdaBoost算法研究与应用[J]. 高技术通讯, 2015, 25(5):437-444.LV Y F, HOU Z J, ZHANG K. Study of multiclass BP-AdaBoost and its application[J]. Chinese High Technology Letters, 2015, 25(5):437-444. (in Chinese) [14] 钱揖丽, 冯志茹. 利用AdaBoost-SVM集成算法和语块信息的韵律短语识别[J]. 计算机工程与科学, 2015, 37(12):2324-2330.QIAN Y L, FENG Z R. Recognition of Chinese prosodic phrases based on AdaBoost-SVM algorithm and chunk information[J]. Computer Engineering and Science, 2015, 37(12):2324-2330. (in Chinese)
2019, Vol. 59 
