Learning Deep Wavelet Networks for Recognition System of Arabic Words
| Autor: | Ridha Ejbali, Salima Hassairi, Amira Bouallégue, Mourad Zaied |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Arabic
Computer science business.industry Speech recognition Deep learning SIGNAL (programming language) Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing) 020206 networking & telecommunications Pattern recognition 02 engineering and technology Construct (python library) Autoencoder Class (biology) language.human_language ComputingMethodologies_PATTERNRECOGNITION Wavelet Computer Science::Sound 0202 electrical engineering electronic engineering information engineering language 020201 artificial intelligence & image processing Mel-frequency cepstrum Artificial intelligence business |
| Zdroj: | International Joint Conference SOCO’16-CISIS’16-ICEUTE’16 ISBN: 9783319473635 SOCO-CISIS-ICEUTE |
| DOI: | 10.1007/978-3-319-47364-2_48 |
| Popis: | In this paper, we propose a new method of learning for speech signal. This technique is based on the deep learning and the wavelet network theories. The goal of our approach is to construct a deep wavelet network (DWN) using a series of Stacked Wavelet Auto-Encoders. The DWN is devoted to the classification of one class compared to other classes of the dataset. The Mel-Frequency Cepstral Coefficients (MFCC) is chosen to select speech features. Finally, the experimental test is performed on a prepared corpus of Arabic words. |
| Databáze: | OpenAIRE |
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