Speech processing applications have become integral components across various domains of modern life. The design and preparation of a reliable recognition system rely heavily on the availability of suitable speech databases. While numerous speech databases exist for English and other languages, the availability of comprehensive resources for Arabic language remains limited. In light of this, we conducted a systematic review aiming to identify, analyse, and classify existing Modern Standard Arabic speech databases. Through our review, we identified 27 publicly available databases and analysed an additional 80 subjective databases. These databases were thoroughly studied, classified based on their characteristics, and subjected to a detailed analysis of research trends in the field. This paper provides a comprehensive discussion on the diverse speech databases developed for various speech processing applications. It sheds light on the purposes and unique characteristics of Arabic speech databases, enabling researchers to easily access suitable resources for their specific applications. The findings of this review contribute to bridging the gap in available Arabic speech databases and serve as a valuable resource for researchers in the field.

Phoneme classification performance is a critical factor for the successful implementation of a speech recognition system. A mispronunciation of Arabic short vowels or long vowels can change the meaning of a complete sentence. However, correctly distinguishing phonemes with vowels in Quranic recitation (the Holy book of Muslims) is still a challenging problem even for state-of-the-art classification methods, where the duration of the phonemes is considered one of the important features in Quranic recitation, which is called Medd, which means that the phoneme lengthening is governed by strict rules. These features of recitation call for an additional classification of phonemes in Qur’anic recitation due to that the phonemes classification based on Arabic language characteristics is insufficient to recognize Tajweed rules, including the rules of Medd. This paper introduces a Rule-Based Phoneme Duration Algorithm to improve phoneme classification in Qur’anic recitation. The phonemes of the Qur’anic dataset contain 21 Ayats collected from 30 reciters and are carefully analyzed from a baseline HMM-based speech recognition model. Using the Hidden Markov Model with tied-state triphones, a set of phoneme classification models optimized based on duration is constructed and integrated into a Quranic phoneme classification method. The proposed algorithm achieved outstanding accuracy, ranging from 99.87% to 100% according to the Medd type. The obtained results of the proposed algorithm will contribute significantly to Qur’anic recitation recognition models.

In this work, the use of agricultural waste from oil palm petioles (OPP) as a raw material for the production of activated carbon (AC) and its characterization were examined. By soaking these chars in nitric acid (HNO3) and potassium hydroxide (KOH) at a 10% concentration, AC with favorable high-porosity carbons was generated. To maximize AC synthesis, the AC was pyrolyzed at 460, 480, and 500 °C temperatures for 20 min. Based on micrographs of formed pores and surface functional groups, 480 °C carbonization temperature on both chemical HNO3 and KOH was shown to be the best. The FTIR measurements reveal that chemical activation successfully transformed the raw material into AC. Moreover, FESEM micrographs show the pores and cavities of the prepared AC achieve a high surface area. This is further supported by BET results of HNO3 OPP AC and KOH OPP AC with surface areas of 883.3 and 372.4 m2/g, respectively, compared with the surface area of raw OPP of 0.58 m2/g. Furthermore, the tests were revealed by an optimization model, namely response surface methodology (RSM), using a central composite design (CCD) technique. The findings showed that all three parameters (pH, time, and dose) had a substantial impact on the removal of Zn, Fe, and Mn. Analysis of variance (ANOVA) and analytical error indicated that the models were accurate, with a low error value and a high R2 > 0.9. Remarkably, the good correlation between actual and predicted removal values showed that the modified activated carbon is a promising adsorbent for heavy metal removal from wastewater.