Erratum to: What we have and what is needed, how to evaluate Arabic Speech Synthesizer?

Doush, Iyad Abu; Alkhateeb, Faisal; Bsoul, Abed Al Raoof

doi:10.1007/s10772-016-9350-8

Cited by 2 publications

(3 citation statements)

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“…TTS systems can be evaluated using subjective and objective tests. Subjective evaluation of speech quality is possible using different methods such as the Mean Opinion Score (MOS) test, diagnostic rhythm test (DRT), categorical estimation evaluation (CME), and Diagnostic Acceptability Measure (DAM) [36], [60]. The MOS test is the most commonly used subjective test, in which human listeners judge the quality of synthesized speech on a scale of one to five [60].…”

Section: Evaluation Of a Tts Systemmentioning

confidence: 99%

“…The MOS test is the most commonly used subjective test, in which human listeners judge the quality of synthesized speech on a scale of one to five [60]. Objective measures, such as the Signal-to-Noise Ratio (SNR), Linear Predictive (LP-based), Cepstrum Distance (CD), and Perceptual Evaluation of Speech Quality (PESQ), can also be used to evaluate speech quality [36]. Speech quality assesses the similarities between synthetic and natural human speech.…”

Section: Evaluation Of a Tts Systemmentioning

confidence: 99%

“…[35] further reported that the iOS speech engine generated more natural results than the Android speech engine, even though Android performed better in terms of intelligibility. [36] evaluated six different speech synthesizer applications available on the Internet for Arabic language: Acapela, ISpeech, Arabi, Sakhr [37], Google, and Nuance, and tested them using objective and subjective evaluations. Five Arabic TTS synthesizers for mobile devices, notably VoiceOver, Uspeech, Acapela, Adel, and SVOX, were evaluated in [38].…”

Section: Introductionmentioning

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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Section: Evaluation Of a Tts Systemmentioning

confidence: 99%

Section: Evaluation Of a Tts Systemmentioning

confidence: 99%