Name
Abstract | ||
|---|---|---|
The performance of an automatic speech recognition (ASR) system is highly degraded in the presence of noise and reverberation. The autoregressive (AR) modeling approach, which preserves the high energy regions of the signal that are less susceptible to noise, first, presents a potential method for robust feature extraction. Second, there are strong correlations in the spectrotemporal domain of the... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LSP.2017.2724561 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Feature extraction,Speech,Spectrogram,Discrete cosine transforms,Noise measurement,Estimation,Speech recognition | Autoregressive model,Reverberation,Pattern recognition,Potential method,Spectrogram,Multivariate statistics,Discrete cosine transform,Feature extraction,Speech recognition,Artificial intelligence,High energy,Mathematics | Journal |
Volume | Issue | ISSN |
24 | 9 | 1070-9908 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sriram Ganapathy | 1 | 252 | 39.62 |