Arabic corpus implementation: Application to speech recognition

Helali, Wafa; Hajaiej, Zied; Cherif, Adnane

doi:10.1109/aset.2018.8379833

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Additionally, there are various works on Arabic/Dialect/Amazigh speech recognition with different approaches, such as [17], [18], [19], [20], [21], [22].…”

Section: Related Workmentioning

confidence: 99%

WITHDRAWN: An Experiment of Moroccan Dialect speech recognition in noisy environments using PocketSphinx

OUISAADANE,

SAFI,

FRIKEL

2023

Preprint

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In this study, we introduce an experimental framework for Moroccan Dialect speech recognition under various additive noise conditions using the open-source tool PocketSphinx. We curated a corpus comprising the ten most commonly used greetings in the Moroccan dialect, extracted from telephone conversations. This corpus was recorded with 60 speakers (30 males and 30 females). Each speaker articulated each expression three times in natural and noisy conditions. Feature extraction utilized Mel Scale Cepstral Coefficients (MFCC), and acoustic modeling, based on monophony, was implemented using Hidden Markov Models (HMM). While automatic speech recognition systems demonstrate commendable performance in noise-free conditions, their efficacy noticeably diminishes in the presence of noise.

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“…Additionally, there are various works on Arabic/Dialect/Amazigh speech recognition with different approaches, such as [17], [18], [19], [20], [21], [22].…”

Section: Related Workmentioning

confidence: 99%

WITHDRAWN: An Experiment of Moroccan Dialect speech recognition in noisy environments using PocketSphinx

OUISAADANE,

SAFI,

FRIKEL

2023

Preprint

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“…Additionally, research has explored the comparison of feature extraction between RASTA-PLP in Arabic letter recognition [9]. Durairaj conducted research by combining various extraction techniques, including LPC, MFCC, and RASTA-PLP [10], whereas Helali [11] utilized MFCC, PLP, and LPC extraction techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the research mentioned above [6], [9]- [11], various classification models have been introduced. However, these studies primarily focus on the makhraj, or sound of the Arabic corpus, rather than the properties of the letters.…”

Section: Introductionmentioning

confidence: 99%

XGBoost and Convolutional Neural Network Classification Models on Pronunciation of Hijaiyah Letters According to Sanad

Azis,

Lestari

2023

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According to Sanad, the pronunciation of Hijaiyah letters can serve as a benchmark for correct or valid reading based on the makhraj and properties of the letters. However, the limited number of Qur'anic Sanad teachers remains one of the obstacles to learning the Qur'an. This study aims to identify the most practical combination of classification models in constructing a voice recognition system that facilitates learning without requiring direct interaction with a teacher. The methods employed include the XGBoost algorithm and CNN. As a result, out of the 12 letter trait labels, the CNN model was utilized for 10 of them, specifically for traits S1, S2, S4, S5, T1, T2, T3, T4, T5, and T6, on trait labels S3 and T7 applying the XGBoost model. Furthermore, the inclusion of additional data yielded performance results for each property, with an average accuracy of 78.14% for property S (letters with opposing properties), 70.69% for property T (letters without opposing properties), and an overall average of 73.79% per letter.

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An experiment of Moroccan dialect speech recognition in noisy environments using PocketSphinx

Ouisaadane,

Safi,

Frikel

2024

Int J Speech Technol

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Arabic corpus implementation: Application to speech recognition

Cited by 5 publications

References 7 publications

WITHDRAWN: An Experiment of Moroccan Dialect speech recognition in noisy environments using PocketSphinx

WITHDRAWN: An Experiment of Moroccan Dialect speech recognition in noisy environments using PocketSphinx

XGBoost and Convolutional Neural Network Classification Models on Pronunciation of Hijaiyah Letters According to Sanad

An experiment of Moroccan dialect speech recognition in noisy environments using PocketSphinx

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