DNN-based grapheme-to-phoneme conversion for Arabic text-to-speech synthesis

Ali, Ikbel Hadj; Mnasri, Zied; Lachiri, Zied

doi:10.1007/s10772-020-09750-7

Cited by 8 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to improve the TSS performance of the GRU model, the AOA technique is used as a hyperparameter optimizer [21]. Like other birds, Aquila has a dark brown colour complexion.…”

Section: Aoa Based Hyperparameter Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People

Alnfiai¹,

Almalki²,

Al‐Wesabi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Text-To-Speech (TTS) is a speech processing tool that is highly helpful for visually-challenged people. The TTS tool is applied to transform the texts into human-like sounds. However, it is highly challenging to accomplish the TTS outcomes for the non-diacritized text of the Arabic language since it has multiple unique features and rules. Some special characters like gemination and diacritic signs that correspondingly indicate consonant doubling and short vowels greatly impact the precise pronunciation of the Arabic language. But, such signs are not frequently used in the texts written in the Arabic language since its speakers and readers can guess them from the context itself. In this background, the current research article introduces an Optimal Deep Learning-driven Arab Text-to-Speech Synthesizer (ODLD-ATSS) model to help the visually-challenged people in the Kingdom of Saudi Arabia. The prime aim of the presented ODLD-ATSS model is to convert the text into speech signals for visually-challenged people. To attain this, the presented ODLD-ATSS model initially designs a Gated Recurrent Unit (GRU)-based prediction model for diacritic and gemination signs. Besides, the Buckwalter code is utilized to capture, store and display the Arabic texts. To improve the TSS performance of the GRU method, the Aquila Optimization Algorithm (AOA) is used, which shows the novelty of the work. To illustrate the enhanced performance of the proposed ODLD-ATSS model, further experimental analyses were conducted. The proposed model achieved a maximum accuracy of 96.35%, and the experimental outcomes infer the improved performance of the proposed ODLD-ATSS model over other DL-based TSS models.

show abstract

“…In order to improve the TSS performance of the GRU model, the AOA technique is used as a hyperparameter optimizer [21]. Like other birds, Aquila has a dark brown colour complexion.…”

Section: Aoa Based Hyperparameter Optimizationmentioning

confidence: 99%

“…Similar to the population-based algorithms, the AOA methodology starts with a population of the candidate solutions. The technique stochastically initiates with an upper limit and a lower limit [21]. Every iteration almost defines the optimal solution as given below.…”

Section: Aoa Based Hyperparameter Optimizationmentioning

confidence: 99%

Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People

Alnfiai¹,

Almalki²,

Al‐Wesabi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…We use two widely used datasets for Arabic NLP [43], [44], [45], [46], [47], [48], [49] to train, validate, and test our proposed BiLSTM models. The first is a processed subset of the Tashkeela corpus as extracted in [50].…”

Section: B Data Setsmentioning

confidence: 99%

Correcting Arabic Soft Spelling Mistakes using BiLSTM-based Machine Learning

Abandah¹,

Suyyagh²,

Khedher³

2022

IJACSA

View full text Add to dashboard Cite

Soft spelling mistakes are a class of mistakes that is widespread among native Arabic speakers and foreign learners alike. Some of these mistakes are typographical in nature. They occur due to orthographic variations of some Arabic letters and the complex rules that dictate their correct usage. Many people forgo these rules, and given the identical phonetic sounds, they often confuse such letters. In this paper, we investigate how to use machine learning to correct such mistakes given that there are no sufficient datasets to train the correction models. Soft errors detection and correction is an active field in Arabic natural language processing. We generate training datasets using proposed transformed input approach and stochastic error injection approach. These approaches are applied to two acclaimed datasets that represent Classical Arabic and Modern Standard Arabic. We treat the problem as character-level, one-to-one sequence transcription problem. This one-to-one transcription of mistakes that include omissions and deletions is possible with adopted simple transformations. This approach permits using bidirectional long short-term memory (BiLSTM) models that are more effective to train compared to other alternatives such as encoder-decoder models. Based on investigating multiple alternatives, we recommend a configuration that has two BiLSTM layers, and is trained using the stochastic error injection approach with error injection rate of 40%. The best model corrects 96.4% of the injected errors and achieves a low character error rate of 1.28% on a real test set of soft spelling mistakes

show abstract

“…Diacritical marks are used in Arabic to differentiate words in terms of sound and meaning. However, Arabic writers rarely use diacritics in their non-academic writing [28], compounding the complexity of text processing in MSA.…”

Section: Introductionmentioning

confidence: 99%

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

Chemnad

Othman

2023

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

DNN-based grapheme-to-phoneme conversion for Arabic text-to-speech synthesis

Cited by 8 publications

References 22 publications

Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People

Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People

Correcting Arabic Soft Spelling Mistakes using BiLSTM-based Machine Learning

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

Contact Info

Product

Resources

About