A speech recognition model based on tri-phones for the Arabic language
العنوان: | A speech recognition model based on tri-phones for the Arabic language |
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المؤلفون: | Al-Diri, B., Ahmad Sharieh, Qutiashat, M. |
المصدر: | Scopus-Elsevier |
بيانات النشر: | Association for the Advancement of Modelling & Simulation Techniques in Enterprises (AMSE), 2007. |
سنة النشر: | 2007 |
مصطلحات موضوعية: | G710 Speech and Natural Language Processing, G700 Artificial Intelligence |
الوصف: | One way to keep up a decent recognition of results- with increasing vocabulary- is the use of base units rather than words. This paper presents a Continuous Speech Large Vocabulary Recognition System-for Arabic, which is based on tri-phones. In order to train and test the system, a dictionary and a 39-dimensional Mel Frequency Cepstrum Coefficient (MFCC) feature vector was computed. The computations involve: Hamming Window, Fourier Transformation, Average Spectral Value (ASV), Logarithm of ASV, Normalized Energy, as well as, the first and second order time derivatives of 13-coefficients. A combination of a Hidden Markov Model and a Neural Network Approach was used in order to model the basic temporal nature of the speech signal. The results obtained by testing the recognizer system with 7841 tri-phones. 13-coefficients indicate accuracy level of 58%. 39-coeefficents indicates 62%. With Cepstrum Mean Normalization, there is an indication of 71%. With these small available data-only 620 sentences-these results are very encouraging. |
وصف الملف: | application/pdf |
اللغة: | English |
URL الوصول: | https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::a77b87c911f4eacd0dffa99d3600e2d0 |
حقوق: | OPEN |
رقم الأكسشن: | edsair.dedup.wf.001..a77b87c911f4eacd0dffa99d3600e2d0 |
قاعدة البيانات: | OpenAIRE |
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