Abstract

This paper reports preliminary results of data-driven modeling of segmental (phoneme) duration for Hindi. Classification and Regression Tree (CART) based datadriven duration modeling for segmental duration prediction is presented. A number of features are considered and their usefulness and relative contribution for segmental duration prediction is assessed. Objective evaluation of the duration model, by root mean squared prediction error (RMSE) and correlation between actual and predicted durations, is performed.

Keywords:
Duration (music) Computer science Mean squared error Artificial intelligence Hindi Regression Speech recognition Data modeling Statistics Natural language processing Pattern recognition (psychology) Mathematics

Metrics

24
Cited By
1.16
FWCI (Field Weighted Citation Impact)
12
Refs
0.81
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Speech Recognition and Synthesis
Physical Sciences →  Computer Science →  Artificial Intelligence
Speech and Audio Processing
Physical Sciences →  Computer Science →  Signal Processing
Music and Audio Processing
Physical Sciences →  Computer Science →  Signal Processing

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Journal:   4th International Conference on Spoken Language Processing (ICSLP 1996) Year: 1996 Pages: 2395-2398
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