Unlocking the Potential of Arabic Voice-Generation Technologies

Potential Application of Arabic TTS.  With more than 422 million^a speakers worldwide, the potential for Arabic voice technologies are vast. In education, AI-powered tools such as ArabicTutor,¹² Numue,^b and QVoice^c,10 provide real-time spoken feedback, making language learning more interactive. For accessibility, services such as NVDA^d and CogentIfotech^e enable visually impaired persons and healthcare recipients (as well as providers) to access digital content and health records. Furthermore, Arabic voice generation facilitates culturally appropriate media content generation and enhances interaction with Arabic large language models (LLMs), such as Fanar,^f for seamless natural communication.

Challenges in Arabic.  Despite all its potential, developing Arabic TTS remains challenging due to the morphological richness, phonetic complexity, and diglossic nature of the language. This complexity is further amplified by Arabic’s unique status as a shared language across 22 countries,^a encompassing more than 20 mutually unintelligible dialects. This gap between Modern Standard Arabic (MSA) and Dialectal Arabic (DA) poses additional challenges for model training, as spoken Arabic varieties differ widely across regions in both phonology and lexicon. Unlike MSA, DA lacks a standardized orthographic norm, making resources inconsistent and difficult to use. Moreover, the absence of diacritics in written Arabic creates pronunciation ambiguities, requiring additional preprocessing, primarily diacritic recovery, to ensure accurate and natural synthesized speech. These linguistic complexities and the scarcity of high-quality Arabic datasets hinder progress in voice-generation models, and addressing these challenges is crucial for advancing Arabic TTS technology.

Diacritization, Datasets, and Model Design.  Despite Arabic’s extensive research history in natural language processing (NLP) and speech recognition,⁸ voice generation remains mostly underexplored. Diacritization is a crucial component in Arabic TTS, with its modeling approaches ranging from rule-based methods to deep learning. State-of-the-art models either use surface, morphological, and syntactic features⁷ or rely exclusively on character sequences, with the latter approach achieving the best results.^11,15 While multilingual LLMs such as ChatGPT and Gemini show some promise for diacritization, Arabic-centric NLP tools continue to yield better results. Tools including Farasa,¹ CAMeL,¹⁷ and MADAMIRA¹⁸ offer critical support for diacritization, with datasets such as Arabic Penn Treebank, CATT, Tashkeela, and WikiNews driving further research.

Arabic voice generation has evolved from rule-based and concatenative methods to advanced deep-learning architectures, significantly improving naturalness and expressiveness. Modern TTS systems typically extract linguistic features from input text (linguistic analyzer), then predict continuous or discrete speech representations (acoustic modeling), and convert them into waveforms (Vocoder). Recent approaches simplify or bypass the linguistic analysis,⁵ thus removing dependency on linguistic resources. Alternatively, some models adopt end-to-end architecture, streamlining the process of generating waveforms directly from textual input. For more technical details see Alrige et al.⁵

A major bottleneck is still the scarcity of high-quality Arabic speech datasets. Several corpora have been developed, including ASC^g—the first open-source Arabic TTS dataset—read speech data as part of the NEMLAR project,²¹ and Classical Arabic TTS.¹³ Additionally, Al-Radhi et al.⁴ created a small audiovisual phonetically annotated Arabic corpus to develop a vocoder, and TunArTTS¹⁴ also introduced a small, single (male) speaker dataset.

Multilingual foundation models have become a powerful catalyst for advancing Arabic TTS. Models including YourTTS and XTTS-v2, trained on diverse multilingual corpora, effectively transfer prosodic and phonetic knowledge to low-resource languages such as Arabic. They significantly reduce the need for large Arabic-specific datasets and enable seamless adaptation across dialects and speakers. To address resource limitations, researchers have fine-tuned pretrained Arabic transformer models such as ArTST¹⁹ for developing a TTS. For a comprehensive review, refer to Alrige et al.⁵ and Chemnad and Othman.⁶ Such adaptations to new voices or dialects can also be achieved with just a handful of training examples, using zero-shot and few-shot voice-generation techniques. Zero- or few-shot learning refers to the model’s ability to replicate voices with minimal data.²⁰ However, their application to Arabic remains largely underexplored. Notably, Doan et al.⁹ used the QASR corpus¹⁶ and XTTS-v2^h to lay the groundwork for future research in low-resource Arabic voice cloning.

The Commercial and Academic Push toward Arabic Speech Technology.  Even though open source Arabic TTS faces resource scarcity, commercial alternatives have flourished. Major technology companies such as Microsoft, Google, Amazon, OpenAI, and IBM provide Arabic TTS solutions in MSA, whereas very few came with dialectal coverage. For example, Microsoft covers 16 Arab-speaking countries, while Amazon Polly covers standard Arabic and GULF Arabic as part of its cloud-based AI services. Other companies, such as iSpeech, ElevenLabs, and Kanari, have also developed MSA Arabic TTS products tailored to various use cases. However, the majority of commercial TTS efforts still face challenges, particularly in handling diacritization. For instance, while Amazon Polly’s Zeina (MSA) voice is regarded as accurate, Abdelali et al.³ found that it performs comparably to QCRI-Kanari TTS in intelligibility and naturalness, yet continues to struggle with accurate diacritization.

Research initiatives from academic institutes—such as NatiQ² and Fanar-TTS, along with tools like Farasa, CaMel, and ArTST—represent some of the most impactful contributions supporting and advancing Arabic speech technology communities. Collaboration between academia, industry, and governments is crucial for further progress, with open source projects playing a pivotal role in democratizing access to Arabic speech resources.

Looking ahead, Arabic voice-generation technologies have immense potential but also present ethical challenges. Ensuring privacy, preventing impersonation, and mitigating disinformation will require audio watermarking, synthetic speech detection, and robust authentication mechanisms. These challenges open new opportunities for research and businesses for the community. Despite these hurdles, continued advances in Arabic TTS and voice cloning can drive societal impact, fostering inclusive communication, enhanced learning, and cultural exchange across the Arabic-speaking world and beyond.