@inproceedings{69be685ece924e84a91e8093e9fdf3ae,
title = "Phonetic segmentation using statistical correction and multi-resolution fusion",
abstract = "This paper focuses on the generation of accurate phonetic segmentations. Statistical methods based on absolute and relative correction are discussed and experimented on both monophone and biphone models to improve the segmentation results. The influence of search range on the statistical correction process is studied and a state selection technique is used to enhance the correction results. This paper also explores the influence of resolution (stepsize) of HMMs and proposes a multi-resolution fusion process to further refine the statistically corrected results. Improvements of segmentation results in terms of segmentation accuracy, mean absolute error (MAE), and root mean square error (RMSE) can be observed by applying the proposed refinement methods.",
keywords = "multi-resolution, phonetic segmentation, state selection, statistical correction",
author = "Sixuan Zhao and Soon, {Ing Yann} and Koh, {Soo Ngee} and Luke, {Kang Kwong}",
year = "2013",
month = oct,
day = "18",
doi = "10.1109/ICASSP.2013.6638957",
language = "English",
isbn = "9781479903566",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "6694--6698",
booktitle = "2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings",
note = "2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 ; Conference date: 26-05-2013 Through 31-05-2013",
}