Research of LDA-Based Two-Dimension Cepstrum Speech Recognition
2009
Hochschulschrift
Zugriff:
97
This thesis proposed a new robust speech recognition technique in noisy environment. The feature extraction bases on Modified Two-Dimension Cepstrum (MTDC), and template matching employs Gaussian Mixture Models (GMM). However, the noisy background in our life may interfere with the performance. Hence, we adopt genetic algorithms (GA)、principal component analysis (PCA) and linear discriminant analysis (LDA) to enhance speech features. Next, we used the system to identify the speech. We adopted numbers in Chinese (0-9) from 10 speakers (5 males and 5 females), then everyone chanted 10 times for each number (total files: 10400). We selected 980 files of each one as the training file, the remainder as the testing files. Finally, we compared and discussed the results which are tested in several variable background noises form different conditions.
Titel: |
Research of LDA-Based Two-Dimension Cepstrum Speech Recognition
|
---|---|
Autor/in / Beteiligte Person: | Jiang, Min-siou ; 江敏秀 |
Link: | |
Veröffentlichung: | 2009 |
Medientyp: | Hochschulschrift |
Sonstiges: |
|