Name
Abstract | ||
|---|---|---|
In this letter, the effectiveness of recently reported SMAC (Spectral Moment time-frequency distribution Augmented by low-order Cepstral) features has been evaluated for robust automatic speech recognition (ASR). The SMAC features consist of normalized first central spectral moments appended with low-order cepstral coefficients. These features have been designed for achieving robustness to both ad... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LSP.2017.2705085 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Speech,Robustness,Mel frequency cepstral coefficient,Hidden Markov models,Additive noise | Truncation,Mel-frequency cepstrum,Normalization (statistics),Pattern recognition,Cepstrum,Speech recognition,Robustness (computer science),Artificial intelligence,Decoding methods,Hidden Markov model,Mathematics,Computation | Journal |
Volume | Issue | ISSN |
24 | 8 | 1070-9908 |
Citations | PageRank | References |
5 | 0.43 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Shahnawazuddin | 1 | 64 | 17.34 |
| Rohit Sinha | 2 | 231 | 30.54 |
| G. Pradhan | 3 | 88 | 13.14 |