2024-03-28T08:55:43Z
https://nagoya.repo.nii.ac.jp/oai
oai:nagoya.repo.nii.ac.jp:00007728
2023-01-16T03:53:11Z
320:321:322
ADAPTIVE REGRESSION BASED FRAMEWORK FOR IN-CAR SPEECH RECOGNITION
Li, Weifeng
21911
Itou, Katunobu
21912
Takeda, Kazuya
21913
Itakura, Fumitada
21914
We address issues for improving hands-free speech recognition performance in different car environments using a single distant microphone. In our previous work, we proposed a regression based enhancement method for in-car speech recognition. In this paper, we describe recent improvements and propose a data-driven adaptive regression based speech recognition system, in which both feature enhancement and model compensation are performed. Based on isolated word recognition experiments conducted in 15 real car environments, the proposed adaptive regression approach shows an advantage in average relative word error rate (WER) reductions of 52.5% and 14.8%, compared to original noisy speech and ETSI advanced front-end, respectively.
journal article
IEEE
2006
application/pdf
IEEE International Conference on Acoustics, Speech and Signal Processing
1
501
504
http://hdl.handle.net/2237/9447
1520-6149
https://nagoya.repo.nii.ac.jp/record/7728/files/takeda_501.pdf
eng
https://doi.org/10.1109/ICASSP.2006.1660067
1-4244-0469-X
Copyright © 2006 IEEE. Reprinted from (relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.