Speech Recognition of Moroccan Dialect Using Hidden Markov Models

Abstract

<p>This paper addresses the development of an Automatic Speech Recognition (ASR) system for the Moroccan Dialect. Dialectal Arabic (DA) refers to the day-to-day vernaculars spoken in the Arab world. In fact, Moroccan Dialect is very different from the Modern Standard Arabic (MSA) because it is highly influenced by the French Language. It is observed throughout all Arab countries that standard Arabic widely written and used for official speech, news papers, public administration and school but not used in everyday conversation and dialect is widely spoken in everyday life but almost never written. we propose to use the Mel Frequency Cepstral Coefficient (MFCC) features to specify the best speaker identification system. The extracted speech features are quantized to a number of centroids using vector quantization algorithm. These centroids constitute the codebook of that speaker. MFCC’s are calculated in training phase and again in testing phase. Speakers uttered same words once in a training session and once in a testing session later. The Euclidean distance between the MFCC’s of each speaker in training phase to the centroids of individual speaker in testing phase is measured and the speaker is identified according to the minimum Euclidean distance. The code is developed in the MATLAB environment and performs the identification satisfactorily.</p>

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“…The job of identifying sound events includes identifying and classifying sounds in audio forecasting and offset sounds for different cases of sound events and offering a textual descriptor for each [13][14]. Common classification method used on SED are as the conventional speech recognition [12].…”

“…Smaller frames will make up more features could lead to overfitting in artificial intelligence systems, which can result in lower precision during classification [17]. Representations of the signal range, including such Mel-frequency coefficients (MFCC), Mel energies, or just the amplitude or energy range are often used in sound event identification [13][14].…”

Thermodynamic Properties of Dense Hydrogen Plasmas With Partially Degenerate Semiclassical Ions

“…MFCCs are extracted from speech signals through cepstral analysis. The input signal is first formed and processed in the form of a window, then the Fourier transform is taken and the value of the resulting spectrum is deformed according to the Mel scale [7].…”

Mel-log energies analysis of authentic audible intrusion activities in a Malaysian forest

“…The features will allow recognition but high dimensional feaetures casues overfitting [20]. The Cepstral features such as MLE mimics the human preception of sound [21]. The feature of MLE has produce well in the past on SED.…”