In the past, we proposed an HMM-based residual echo modeling technique that proved to be effective in eliminating false triggering and enhanced the recognition of valid keyword speech. The narrow training strategy based on single recorded prompt made the echo model a better discriminator, though it has the possible drawback of requiring retraining if the prompt changes. To overcome this intrinsic problem, we consider building a general echo model that is trained over many sentences from varied voices. The experimental results show that the multiple echo models in conjunction with suitable filler model to represent the extraneous speech not only provide good recognition accuracy but also yield better out-of-vocabulary rejection.
Cite as: Chengalvarayan, R., Thomson, D.L. (2000) HMM-based echo and announcement modeling approaches for noise suppression avoiding the problem of false triggers. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 1, 754-757, doi: 10.21437/ICSLP.2000-187
@inproceedings{chengalvarayan00_icslp,
author={Rathinavelu Chengalvarayan and David L. Thomson},
title={{HMM-based echo and announcement modeling approaches for noise suppression avoiding the problem of false triggers}},
year=2000,
booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
pages={vol. 1, 754-757},
doi={10.21437/ICSLP.2000-187}
}