Statistical Formant Speech Synthesis for Arabic

Jafri, Afshan; Sobh, Ibrahim; Alkhairy, A.

doi:10.1007/s13369-015-1771-1

Cited by 6 publications

(4 citation statements)

References 23 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%

“…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. Most studies measured intelligibility [91], [94], [95], [96], [99], [103], [104], [105], [61], [106], [109], [110], [112], [115], [116], [118], [120] and naturalness [94], [95], [99], [101], [103], [104], [105], [108], [109], [112], [115], [116], [118] whereas others measured pronunciation [95], [109], sound quality [95], [109], [111], prosody [91], nasality [87], graveness [87], compactness [87], clearness ...…”

Section: ) Rq5: Evaluation Techniques and Resultsmentioning

confidence: 99%

“…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]. Some studies have also evaluated their TTS systems based on a success rate formula [100], [109], [120].…”

Section: ) Rq5: Evaluation Techniques and Resultsmentioning

confidence: 99%

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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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Section: ) Rq5: Evaluation Techniques and Resultsmentioning

confidence: 99%

Section: ) Rq5: Evaluation Techniques and Resultsmentioning

confidence: 99%

Section: ) Rq5: Evaluation Techniques and Resultsmentioning

confidence: 99%

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

Chemnad

Othman

2023

IEEE Access

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“…In Table 1, the rows represent the height of the tongue's position while Dataset: A large scale Arabic single speaker corpus the columns represent its backness. In Table 2, the rows (Abdo et al, 2014;Jafri et al, 2015;Almosallam et al, represent different manners of articulation while the 2013) was used in the implemented experiments. A total of columns represent different placess of articulation 7 h of recordings represented in 4372 wav files were used.…”

Section: Decision Tree Based State Tyingmentioning

confidence: 99%

Large Vocabulary Arabic Continuous Speech Recognition using Tied States Acoustic Models

Azim¹,

Asayesh²,

Badr³

et al. 2019

Asian J. Inf. Technol.

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The Hidden Markov Model (HMM) lies at the heart of the modern speech recognition systems as it provides a simple, effective and straight forward frame work to model the time varying acoustic features of the speech signals. The basic process of building HMM based speech recognition systems is a straight forward process. Nevertheless, the proper parameter estimation of such models requires large training data. Therefore, parameter tying techniques were developed to reduce the parameters of HMMs without affecting the overall system performance. This study proposes an Arabic phonetic decision tree necessary to build Tied State tri-phone HMMs. Experimental results show promising word correctness when compared with both data driven tri-phone models and phoneme based models. The maximum word correctness achieved by the proposed approach was 95.13%. Whereas it reached 78.03 and 58.45% using data driven tri-phones and phoneme based HMMs, respectively, when tested on the same benchmark database.

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