Identification of prosodic phrases based on syntax dependency and conditional random fields

QIAN Yili; ZHANG Ermeng

doi:10.16511/j.cnki.qhdxxb.2018.26.024

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Journal of Tsinghua University(Science and Technology) ›› 2019, Vol. 59 ›› Issue (7) : 530-536. DOI: 10.16511/j.cnki.qhdxxb.2018.26.024

COMPUTER SCIENCE AND TECHNOLOGY

Identification of prosodic phrases based on syntax dependency and conditional random fields

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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%.

Key words

rhythmic phrases / syntax dependency / text features / conditional random fields (CRFs)

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QIAN Yili, ZHANG Ermeng. Identification of prosodic phrases based on syntax dependency and conditional random fields[J]. Journal of Tsinghua University(Science and Technology). 2019, 59(7): 530-536 https://doi.org/10.16511/j.cnki.qhdxxb.2018.26.024

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