1. Hsin-Ju Hsieh 謝欣汝, Wen-hsiang Tu 杜文祥, Jeih-weih Hung 洪志偉 暨南國際大學電機工程學系 報告者：汪逸婷 2012/03/20

2. 1. Introduction 2. Introduction of DCT and the effect of noise on the DCT of the speech feature streams 3. The propposed DCT-base compensation approaches 4. The recognition experiment results and discussions 5. Summary 6. Conclusion and Future Work

3.  Two type of enviornmental distortions ： ◦ Channel distortion ◦ Additive noise  Feature Compensation ◦ Normalize the statistics of temporal domain feature sequence. ◦ Reduce the mismatch by enhancing some components which are not easily affected by noise.  DFT

4.  Discrete Cosine Transform(DCT) ◦ Fourier related transform similar to DFT. ◦ One of the most powerful analysis tool of signal processing. ◦ Transform coding and speech feature extraction  Transform the signal from the time domain into the frequency domain.(periodicity)  Reducing the correlation of features and results in a more compact feature representation.

16.  AURORA 2.0 database  13-dimensional MFCC(c0~c12) sequence  13 new features + first 13 + second-order 13 = final 39-dimensional feature vector

27. 1. Performing DCT-magnitude substitution adaptively 2. Integrating the proposed new methods with some other feature normalization techniques 3. Investigating how to determine the optimal trade-off between the noise reduction and the speech distortion that always exists among the noise-robustness techniques