A new method for language recognition based on improved GMM
| Autor: | Jahanshah Kabudian, Seyed Iman Mousavian, Mansoor Vali, Seyed Mohammad Sadeghi |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Artificial neural network
Computer science business.industry Speech recognition Feature vector SIGNAL (programming language) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Mixture model Data modeling symbols.namesake ComputingMethodologies_PATTERNRECOGNITION symbols Artificial intelligence Hidden Markov model business Gaussian process Language recognition |
| Zdroj: | ICSIPA |
| DOI: | 10.1109/icsipa.2011.6144165 |
| Popis: | The automatic recognition of speech language is called diagnosis of language by signals. These systems often make decision by comparing the privilege of speech signal dependence to various languages. A new method has been applied to improve the results of the automatic recognition of language in this article that acts based on the improved Gaussian Mixture Model (GMM) model. A GMM model is trained by non-overlapping data through the method by applying SDC selective feature vectors. By comparing between this method and the usual GMM fulfilled to diagnose the 4 languages, we have achieved an average improvement of 4.4% in determining language recognition accuracy. At the end of this article the neural network (NN) method has been used as a back-end processing (BEP) method. By applying BEP, recognition error has reduced to 10% in the usual GMM, and to 2.5% in the improved GMM. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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