Name
Abstract | ||
|---|---|---|
This paper proposes a genetic algorithm (GA) based beamformer to optimize speech recognition accuracy for a pretrained speech recognizer. The proposed beamformer is designed to tackle the non-differentiable and non-linear natures of speech recognition by employing the GA algorithm to search for the optimal beamformer weights. Specifically, a population of beamformer weights is reproduced by crossover and mutation until the optimal beamformer weights are obtained. Results show that the speech recognition accuracies can be greatly improved even in noisy environments. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/NSS.2009.44 | NSS |
Keywords | Field | DocType |
noisy environment,ga algorithm,speech recognition,pretrained speech recognizer,optimal beamformer weight,proposed beamformer,genetic algorithm,speech recognition accuracy,non-linear nature,speech recognition enhancement,beamformer weight,noise,accuracy,genetic algorithms,speech,beamforming | Speech enhancement,Beamforming,Population,Crossover,Computer science,Signal enhancement,Speech recognition,Genetic algorithm | Conference |
Citations | PageRank | References |
1 | 0.40 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Y. Chan | 1 | 1 | 0.40 |
| S. Y. Low | 2 | 1 | 0.74 |
| S. Nordholm | 3 | 101 | 11.37 |
| K. F. C. Yiu | 4 | 1 | 0.74 |
| S. H. Ling | 5 | 609 | 40.29 |