Text-to-speech (TTS) systems handle code-switching—switching between languages or dialects in a single sentence—by combining multilingual training data, language-specific processing modules, and contextual awareness. Modern TTS models, such as those based on neural networks, are trained on datasets that include code-switched speech, allowing them to recognize and reproduce patterns where languages blend. For example, a system might process a sentence like “I want to eat sushi tonight, pero no tengo dinero” by first identifying the language boundaries between English and Spanish, then applying appropriate pronunciation rules for each segment.
To manage pronunciation, TTS systems often use separate phoneme inventories or grapheme-to-phoneme (G2P) models for each language. When a language switch is detected, the system dynamically selects the correct phonetic rules. For instance, in the word “sushi,” the “sh” sound follows Japanese phonetics, while “dinero” uses Spanish vowel sounds. Some systems employ language identification at the word or subword level, tagging each token with a language code before synthesis. This ensures that acoustic models—such as those governing intonation or syllable stress—adjust accordingly. Additionally, prosody models may be designed to blend language-specific rhythm patterns, avoiding unnatural pauses or pitch shifts between languages.
Architectural choices also play a role. Transformer-based TTS models, which use self-attention mechanisms, excel at capturing long-range dependencies, making them better suited for code-switching. For example, a model might learn to emphasize the transition between languages by adjusting attention weights during speech generation. Developers can further optimize performance by fine-tuning on code-switched datasets or incorporating explicit language embeddings to guide synthesis. Testing often involves evaluating metrics like pronunciation accuracy and naturalness in mixed-language sentences. While challenges remain—such as handling rare language pairs—advances in multilingual training and modular design continue to improve code-switching capabilities in TTS systems.