Name
Abstract | ||
|---|---|---|
This paper describes a method of compensating for nonlinear distortions in speech representation caused by noise. The method described here is based on the histogram equalization method often used in digital image processing. Histogram equalization is applied to each component of the feature vector in order to improve the robustness of speech recognition systems. The paper describes how the proposed method can be applied to robust speech recognition and it is compared with other compensation techniques. The recognition experiments, including results in the AURORA II framework, demonstrate the effectiveness of histogram equalization when it is applied either alone or in combination with other compensation techniques. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TSA.2005.845805 | IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING |
Keywords | Field | DocType |
cepstral mean normalization, histogram equalization, mean and variance normalization, Mel frequency cepstral coefficients, probability density function (pdf), robust speech recognition, vector Taylor series approach | Speech enhancement,Speech processing,Histogram,Pattern recognition,Computer science,Histogram matching,Adaptive histogram equalization,Speech recognition,Artificial intelligence,Balanced histogram thresholding,Histogram equalization,Color normalization | Journal |
Volume | Issue | ISSN |
13 | 3 | 1063-6676 |
Citations | PageRank | References |
125 | 5.68 | 22 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ángel de la Torre | 1 | 482 | 34.91 |
| Antonio M. Peinado | 2 | 376 | 41.97 |
| José C. Segura | 3 | 481 | 38.14 |
| José L. Pérez-Córdoba | 4 | 175 | 15.79 |
| M. Carmen Benítez | 5 | 303 | 25.05 |
| Antonio J. Rubio | 6 | 466 | 36.14 |