@inproceedings{lin2009a,
author = {Lin, Hui and Deng, Li and Lee, Chi-Hui and Yu, Dong and Acero, Alex and Gong, Yifan},
title = {A Study on Multilingual Acoustic Modeling For Large Vocabulary ASR},
booktitle = {Proceedings of the ICASSP},
year = {2009},
month = {April},
abstract = {We study key issues related to multilingual acoustic modeling for automatic speech recognition (ASR) through a series of large-scale ASR experiments. Our study explores shared structures embedded in a large collection of speech data spanning over a number of spoken languages in order to establish a common set of universal phone models that can be used for large vocabulary ASR of all the languages seen or unseen during training. Language-universal and language-adaptive models are compared with language-specific models, and the comparison results show that in many cases it is possible to build general-purpose language-universal and language-adaptive acoustic models that outperform language-specific ones if the set of shared units, the structure of shared states, and the shared acoustic-phonetic properties among different languages can be properly utilized. Specifically, our results demonstrate that when the context coverage is poor in language-specific training, we can use one tenth of the adaptation data to achieve equivalent performance in cross-lingual speech recognition.},
publisher = {Institute of Electrical and Electronics Engineers, Inc.},
url = {http://approjects.co.za/?big=en-us/research/publication/a-study-on-multilingual-acoustic-modeling-for-large-vocabulary-asr/},
edition = {Proceedings of the ICASSP},
}