WASET
	@article{(Open Science Index):https://publications.waset.org/pdf/1192,
	  title     = {Continuous Feature Adaptation for Non-Native Speech Recognition},
	  author    = {Y. Deng and  X. Li and  C. Kwan and  B. Raj and  R. Stern},
	  country	= {},
	  institution	= {},
	  abstract     = {The current speech interfaces in many military
applications may be adequate for native speakers. However,
the recognition rate drops quite a lot for non-native speakers
(people with foreign accents). This is mainly because the nonnative
speakers have large temporal and intra-phoneme
variations when they pronounce the same words. This
problem is also complicated by the presence of large
environmental noise such as tank noise, helicopter noise, etc.
In this paper, we proposed a novel continuous acoustic feature
adaptation algorithm for on-line accent and environmental
adaptation. Implemented by incremental singular value
decomposition (SVD), the algorithm captures local acoustic
variation and runs in real-time. This feature-based adaptation
method is then integrated with conventional model-based
maximum likelihood linear regression (MLLR) algorithm.
Extensive experiments have been performed on the NATO
non-native speech corpus with baseline acoustic model trained
on native American English. The proposed feature-based
adaptation algorithm improved the average recognition
accuracy by 15%, while the MLLR model based adaptation
achieved 11% improvement. The corresponding word error
rate (WER) reduction was 25.8% and 2.73%, as compared to
that without adaptation. The combined adaptation achieved
overall recognition accuracy improvement of 29.5%, and
WER reduction of 31.8%, as compared to that without
adaptation.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {1},
	  number    = {6},
	  year      = {2007},
	  pages     = {1701 - 1708},
	  ee        = {https://publications.waset.org/pdf/1192},
	  url   	= {https://publications.waset.org/vol/6},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 6, 2007},
	}