Duration Modeling Using Multi-model Based on Positional Information

  • Vempada Ramu Reddy
  • Krothapalli Sreenivasa Rao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)


This paper proposes prediction of syllable durations by developing multi-models using positional information. The proposed multi-model consists of four models used for predicting the durations of syllables. Among them, one of the models is used for predicting the durations of syllables present in mono-syllabic words, and the remaining three models are meant for predicting the durations of syllables present at initial, middle and final positions of polysyllabic words. In this study, (i) linguistic constraints represented by positional, contextual and phonological features and (ii) production constraints represented by articulatory features are used for predicting the duration patterns. Feed-forward Neural Networks (FFNN) are used for developing the duration models using above mentioned features. It was found, that the prediction accuracy is improved using multi-models compared to single duration model.


Multi-models Duration prediction Prediction accuracy Feed-forward neural networks Linguistic and Production constraints 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Vempada Ramu Reddy
    • 1
  • Krothapalli Sreenivasa Rao
    • 1
  1. 1.School of Information TechnologyIndian Institute of Technology KharagpurKharagpurIndia

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