The introduction of ubiquitous computing and networking has fostered automatic speech recognition (ASR) systems of a distributed nature. The major challenge in deploying ubiquitous ASR is that the operating environments may change rapidly leaving the ASR system very vulnerable. This paper deals with the concept of making ASR systems context-aware with the aim of improving robustness against varying conditions such as dynamic network constraints and environmental noise. To fully benefit from a variety of networks with different characteristics, a number of distributed speech recognition (DSR) schemes are presented each of which is applicable to a specific network context. To increase ASR system robustness in varying environmental noise context, a multiple-model framework for noise-robust ASR is presented where multiple HMM model sets are trained, one for each noise type and each specific signal-to-noise ratio (SNR) that characterise the noise context. Experimental results show that the performance of ASR is largely improved by exploiting the context information.
Cite as: Tan, Z.-H., Dalsgaard, P., Lindberg, B., Xu, H. (2005) Robust speech recognition in ubiquitous networking and context-aware computing. Proc. Interspeech 2005, 2849-2852, doi: 10.21437/Interspeech.2005-834
@inproceedings{tan05_interspeech,
author={Zheng-Hua Tan and Paul Dalsgaard and Børge Lindberg and Haitian Xu},
title={{Robust speech recognition in ubiquitous networking and context-aware computing}},
year=2005,
booktitle={Proc. Interspeech 2005},
pages={2849--2852},
doi={10.21437/Interspeech.2005-834}
}