The use of huge databases in ASR has become an important source of ASR system improvements in the last years. However, their use demands an increase of the computational resources necessary to train the recognizers. Several techniques have been proposed in the literature with the purpose of making a better use of these enormous databases by selecting the most 'informative' portions and thus reducing the computational burden. In this paper, we present a technique to select samples from a database that allows us to obtain similar results in MLP-based feature extraction stages by using around 60% of the data.
Cite as: Pelaez-Moreno, C., Zhu, Q., Chen, B.Y., Morgan, N. (2005) Automatic data selection for MLP-based feature extraction for ASR. Proc. Interspeech 2005, 229-232, doi: 10.21437/Interspeech.2005-115
@inproceedings{pelaezmoreno05_interspeech,
author={Carmen Pelaez-Moreno and Qifeng Zhu and Barry Y. Chen and Nelson Morgan},
title={{Automatic data selection for MLP-based feature extraction for ASR}},
year=2005,
booktitle={Proc. Interspeech 2005},
pages={229--232},
doi={10.21437/Interspeech.2005-115}
}