5th International Conference on Spoken Language Processing

Sydney, Australia
November 30 - December 4, 1998

Speech Recognition In Car Noise Environments Using Multiple Models According To Noise Masking Levels

Myung Gyu Song (1), Hoi In Jung (1), Kab-Jong Shim (2), Hyung Soon Kim (1)

(1) Dept. of Electronics Eng., Pusan National Univ., Korea
(2) Passenger Car E&R Center II, Hyundai Motor Company, Korea

In speech recognition for real-world applications, the performance degradation due to the mismatch introduced between training and testing environments should be overcome. In this paper, to reduce this mismatch, we provide a hybrid method of spectral subtraction and residual noise masking. We also employ multiple model approach to obtain improved robustness over various noise environments. In this approach, multiple model sets are made according to several noise masking levels and then a model set appropriate for the estimated noise level is selected automatically in recognition phase. According to speaker independent isolated word recognition experiments in car noise environments, the proposed method using model sets with only two masking levels reduces average word error rate by 60% in comparison with spectral subtraction method.

Full Paper

Bibliographic reference.  Song, Myung Gyu / Jung, Hoi In / Shim, Kab-Jong / Kim, Hyung Soon (1998): "Speech recognition in car noise environments using multiple models according to noise masking levels", In ICSLP-1998, paper 1065.