Name
Title | ||
|---|---|---|
Robust Feature Extraction Based on Teager-Entropy and Half Power Spectrum Estimation for Speech Recognition. |
Abstract | ||
|---|---|---|
In this paper, we present a robust feature extraction scheme for speech recognition. Compared to standard mel-frequency cepstral coefficients (MFCC), it incorporates perceptual information into half parameter spectrum not into the whole classical spectrum, and combines with Teager-Entropy to construct a new feature vector. Its performance is compared with several techniques, and detailed comparative performance analysis with various types of noise and a wide range of SNR values is presented. The results suggest that our feature achieves superior robustness with HMM-based recognizer on an English digit task. The 8.87 % reduction of average error rate is obtained in comparison to ordinary MFCC. Furthermore, the results also uncover that the half power spectrum-based method leads to superior performance over the whole power spectrum-based method in most given environment. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26181-2_9 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Entropy,MFCC,Spectral analysis,Speech recognition | Perceptual information,Mel-frequency cepstrum,Feature vector,Pattern recognition,Computer science,Word error rate,Feature extraction,Robustness (computer science),Speech recognition,Spectral density,Artificial intelligence,Hidden Markov model | Conference |
Volume | ISSN | Citations |
9426 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Dong | 1 | 0 | 1.01 |
| Dongsheng Zhou | 2 | 2 | 0.73 |
| Qiang Zhang | 3 | 292 | 45.54 |