Integration of Phoneme-Subspaces Using ICA for Speech Feature Extraction and Recognition

@article{Park2008IntegrationOP,
  title={Integration of Phoneme-Subspaces Using ICA for Speech Feature Extraction and Recognition},
  author={Hyunsin Park and Tetsuya Takiguchi and Yasuo Ariki},
  journal={2008 Hands-Free Speech Communication and Microphone Arrays},
  year={2008},
  pages={148-151}
}
In our previous work, the use of PCA instead of DCT shows robustness in distorted speech recognition because the main speech element is projected onto low-order features, while the noise or distortion element is projected onto high-order features [1]. This paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and their elements are statistically mutual independent. The proposed speech feature vector is generated by projecting… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-7 of 7 references

Efplication to Noise Robust Speech Recognition," Joural fect of PCA Filter in Blind Source Separation

  • K. Hermus, P. Wambacq, +6 authors T. Matsui
  • Proc. on Advances in Signal Processing, vol. 2007…
  • 2007
1 Excerpt

Im- 2006. proved MFCC Feature Extraction by PCA-Optimized Filter Bank for Speech Recognition

  • T. Takiguchi, Y. Ariki, +6 authors L-S. Lee
  • Proc. ASRU2001,
  • 2001
2 Excerpts

proved MFCC Feature Extraction by PCA - Optimized Filter Bank for Speech Recognition

  • H. Asoh Y Motomura, T. Matsui

Similar Papers

Loading similar papers…