Automatic Detection of Transition Zones in Tunisian Dialect
نویسندگان
چکیده
منابع مشابه
Automatic Detection of Transition Zones in Tunisian Dialect
This study is an extension of our last researches about the detection of transition zones based on multiresolution spectral analysis (MRS). In this paper we present the fourth step for the realization of an automatic system for Tunisian Dialect segmentation and analysis. The MRS is calculated over several Fast Fourier Transforms (FFT) of different length. It can provide a higher temporal accura...
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Speech recognition for under-resourced languages represents an active field of research during the past decade. The tunisian arabic dialect has been chosen as a typical example for an under-resourced Arabic dialect. We propose, in this paper, our first steps to build an automatic speech recognition system for Tunisian dialect. Several Acoustic Models have been trained using HMM-GMM and HMM-DNN ...
متن کاملThe Multiresolution Spectral Analysis for Automatic Detection of Transition Zones
This paper presents an automatic method for detecting transition zones based on multiresolution spectral analysis (MRS). The MRS is calculated over several Fast Fourier Transforms (FFT) of different length. It can provide a higher temporal accuracy in the upper spectral region and a better frequency resolution in the lower spectral range. We showcase the importance of this tool by attempting an...
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In this paper, we address the problem of the morphological analysis of an Arabic dialect. We propose a method to adapt an Arabic morphological analyzer for the Tunisian dialect (TD). In order to do that, we create a lexicon for the TD. The creation of the lexicon is done in two steps. The first step consists in adapting a Modern Standard Arabic (MSA) lexicon. We adapted a list of MSA derivation...
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In this paper, we investigate different approaches for dialect identification in Arabic broadcast speech. These methods are based on phonetic and lexical features obtained from a speech recognition system, and bottleneck features using the i-vector framework. We studied both generative and discriminative classifiers, and we combined these features using a multi-class Support Vector Machine (SVM...
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ژورنال
عنوان ژورنال: International Journal of Advanced Science and Technology
سال: 2013
ISSN: 2005-4238,2005-4238
DOI: 10.14257/ijast.2013.60.07