Current text-to-speech (TTS) systems typically support between 50 to over 100 languages, depending on the provider and how they define language support. Major cloud-based services like Google Cloud Text-to-Speech, Amazon Polly, and Microsoft Azure TTS lead in language coverage, but the count often includes regional variants and accents. For example, Microsoft Azure claims support for 129 languages and variants, while Google offers over 50 languages with additional accents. These numbers reflect both widely spoken languages (e.g., English, Mandarin, Spanish) and some lower-resource languages, though coverage for the latter remains limited due to data scarcity.
The variation in supported languages stems from differences in data availability and technical approaches. High-resource languages like English benefit from extensive datasets of paired audio and text, enabling high-quality voice synthesis. In contrast, low-resource languages often lack sufficient training data, making them harder to support. Some providers use multilingual models or transfer learning to extend coverage with less data. For instance, a model trained on multiple languages can adapt to a new language using minimal examples. However, quality may suffer compared to languages with dedicated training. Open-source projects like Coqui TTS or Mozilla TTS typically support fewer languages, relying on community contributions for datasets and models.
A critical distinction lies in how providers count languages versus regional variants. For example, English might be split into U.S., U.K., Australian, or Indian accents, each counted as separate entries. Google’s 380 voices across 50+ languages include such variants, while Amazon Polly’s 29 languages encompass fewer dialects. Developers should verify whether a provider’s language list refers to base languages or includes regional flavors. Additionally, niche or endangered languages are rarely supported, highlighting gaps in TTS accessibility. For most applications, major providers cover the majority of global users, but projects targeting specific regions or less common languages may need custom solutions or data collection.