International Symposium on Chinese Spoken Language Processing
August 23-24, 2002
Comparisons of MLLR and CDCN for Speech Recognition in Additive Noise by Experiments
Guo-Hong Ding, Chengrong Li, Bo Xu
Chinese Academy of Sciences, Beijing, China
This paper investigates the problem of robust speech recognition
in additive noise. Model compensation and cepstral feature
compensation techniques are evaluated and compared by experiments.
The approaches considered here are MLLR and CDCN. Both
approaches can be combined with CMN, which is a simple but
efficient approach for robust speech recognition. Different
combinations of CDCN and CMN are investigated in this paper.
Noisy speech is simulated by adding different noise to clean
speech with different SNR. Experiments are implemented on an
isolated word recognition system. And the experimental results
show that MLLR can give better performance in clean and light
degraded environments, while CDCN can provide better in degraded
Ding, Guo-Hong / Li, Chengrong / Xu, Bo (2002):
"Comparisons of MLLR and CDCN for speech recognition in additive noise by experiments",
In ISCSLP 2002, paper 80.