• DocumentCode
    3124012
  • Title

    Prosody-based sentence boundary detection in Chinese broadcast news

  • Author

    Lei Xie ; Chenglin Xu ; Xiaoxuan Wang

  • Author_Institution
    Shaanxi Provincial Key Lab. of Speech & Image Inf. Process., Northwestern Polytech. Univ., Xi´an, China
  • fYear
    2012
  • fDate
    5-8 Dec. 2012
  • Firstpage
    261
  • Lastpage
    265
  • Abstract
    In this paper, we explore the use of prosodic features in sentence boundary detection in Chinese broadcast news. The prosodic features include speaker turn, music, pause duration, pitch, energy and speaking rate. Specifically, considering the Chinese tonal effects in pitch trajectory, we propose to use tone-normalized pitch features. Experiments using decision trees demonstrate that the tone-normalized pitch features show superior performance in sentence boundary detection in Chinese broadcast news. Furthermore, feature combination is able to achieve apparent performance improvement by intuitive feature interactive rules formed in the decision tree. Pause duration and a tone-normalized pitch feature contribute the most part of the feature usage in the best-performing decision tree.
  • Keywords
    decision trees; feature extraction; natural language processing; speech recognition; Chinese broadcast news; Chinese tonal effects; decision trees; energy; intuitive feature interactive rules; music; pause duration; prosody-based sentence boundary detection; speaker turn; speaking rate; tone-normalized pitch features; Decision trees; Feature extraction; Speech; Speech processing; Speech recognition; Testing; Trajectory; rich transcription; sentence boundary detection; sentence segmentation; speech prosody;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Chinese Spoken Language Processing (ISCSLP), 2012 8th International Symposium on
  • Conference_Location
    Kowloon
  • Print_ISBN
    978-1-4673-2506-6
  • Electronic_ISBN
    978-1-4673-2505-9
  • Type

    conf

  • DOI
    10.1109/ISCSLP.2012.6423471
  • Filename
    6423471