DNN-Based Arabic Speech Synthesis

Amrouche, Abderrahmane; Bentrcia, Youssouf; Boubakeur, Khadidja Nesrine; Abed, Abir El

doi:10.1109/iceee55327.2022.9772602

Cited by 11 publications

(3 citation statements)

References 22 publications

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“…Table XII lists the techniques used to evaluate the studies. The subjective evaluation method was commonly applied to evaluate TTS systems, and the most commonly used method was the Mean Opinion Score (MOS) test [88], [92], [93], [94], [99], [103], [104], [105], [61], [106], [108], [110], [112], [114], [115], [117], [118], [119]. Categorial estimation (CE) tests, preference test [102], [108], [117], DMOS test [108], and DRT tests [87], [96], [116] have also been used as subjective evaluation tests in some studies.…”

Section: ) Rq5: Evaluation Techniques and Resultsmentioning

confidence: 99%

“…Some studies were evaluated on words, triphones, and diphones [93], some were evaluated on both words and sentence synthesis [109], [112] while others were evaluated on one of them. Objective tests that were commonly used to evaluate include Visual Perceptual Test [96], PESQ test [61], [110], [116], [119], Mel cepstral distortion (MCD) [98], [99], [102], [106] and Root Mean Squared Error (RMSE) of energy, pitch, and duration models [99], [106], [108], [117]. Some studies have also compared their TTS systems with other existing TTS systems for a more objective evaluation [87], [91], [96], [103], [106], [111], [114], [117].…”

Section: ) Rq5: Evaluation Techniques and Resultsmentioning

confidence: 99%

“…-MSA [118] DNN synthesis Arabic corpus by Halabi et al [125] Takes diacritic text as input MSA [119] DNN synthesis Arabic corpus by [126] which was recorded in a professional studio in south Levantine Arabic - [120] Multi-agent 800 Arabic sentences retrieved from the Arabic corpus by Halabi 2016 [126] -…”

Section: Classical Arabicmentioning

confidence: 99%

See 2 more Smart Citations

Advancements in Arabic Text-to-Speech Systems: A 22-Year Literature Review

Chemnad

Othman

2023

IEEE Access

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Although there are several speech synthesis models available for different languages tailored to specific domain requirements and applications, there is currently no readily available information on the latest trends in Arabic language speech synthesis. This can make it challenging for beginners to research and develop text-to-speech (TTS) systems for Arabic languages. To address this issue, this article provides a comprehensive overview of several scholars' contributions to the field of Arabic TTS, along with an examination of the unique features of the Arabic language and the corresponding challenges in creating TTS systems. Reporting only on papers discussing Arabic TTS, this systematic review evaluated the available literature published between 2000 and 2022. We conducted a systematic review in six databases using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines to identify studies that addressed Arabic Text-to-Speech systems. Of a total of 3719 articles identified, only 36 (0.96%) articles met our search criteria. Bibliometric analyses of these studies were conducted and reported. The results highlight the main types of speech synthesis techniques used in TTS systems: concatenative, formant, deep neural network (DNN), hybrid models, and multiagent. The corpora used to develop these systems, as well as the diacritization techniques incorporated, evaluation techniques, and the results of the performance of the systems are reported. Subjective evaluation using the mean opinion score is most applied to measure the accuracy of systems. This study also identifies gaps in the literature and makes recommendations for future research directions.

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