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. 

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Modern Standard Arabic Speech Corpora: A Systematic Review

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.

In this study, we completed our previous work about rhythm-based investigation of Saudi speech dialects using a corpus called Saudi Accented Arabic Voice Bank (SAAVB). In this continuation paper speech rhythm of the remain four regions that are Eastern Region (ER), Madinah, Hail, and Asier was investigated. Analyses of the past and current results for all nine dialectal regions was conducted. The relations between different interval measures (IMs) and their capabilities of classifying native origins of speakers, their gender (male/female), and type of sentence (question or statement) are studied. SAAVB corpus was used for all experiments. It contains a set of Arabic speech that represents native Arabic speakers from all the cities around Saudi Arabia. A number of rhythm metrics, namely, $\Delta {\mathbf{V}}, \Delta$C, and %V, are calculated and studied in detail. The results show that $\Delta$V metric can differentiate between question and statement sentences. Moreover, the %V detects a significant difference between male and female speakers. The results of the study demonstrate the uniqueness of the Riyadh dialect. In other words, the Riyadh, ER and Asier dialect is different from all other investigated dialects. In Addition to that, a high similarity is observed between the dialects of Madinah and Hail. these two dialects are geographically neighbors and both located in north of Saudi Arabia. Finally, a conclusion and general discussion on speech rhythm metrics is provided.

Rana F. Bin Ikrish

Papers

Extended Speech Rhythm-Based Analysis of Saudi Dialects Using SAAVB Corpus