To develop a text-to-speech (TTS) model for a new language, three core resources are required: data, computational infrastructure, and linguistic expertise. Each plays a critical role in ensuring the model’s accuracy, naturalness, and adaptability to the target language.
First, high-quality data is the foundation. You need a large, diverse corpus of recorded speech in the target language, paired with accurate transcriptions. This dataset should cover different dialects, speaking styles (e.g., formal vs. casual), and speaker demographics (age, gender) to ensure robustness. For under-resourced languages, collecting this data can be challenging and may require partnerships with native speakers or communities. Tools for audio preprocessing (noise removal, normalization) and text processing (tokenization, phonetic alignment) are also essential. For example, a language with complex tonal features, like Mandarin, requires precise pitch annotations, while languages with agglutinative structures, like Turkish, need careful handling of word boundaries.
Second, computational resources are necessary for training and fine-tuning the model. Modern neural TTS systems (e.g., Tacotron, FastSpeech) require significant GPU/TPU power, especially for large datasets. Training a baseline model might take weeks on multiple high-end GPUs, depending on the architecture. Cloud services like AWS or Google Cloud can reduce upfront hardware costs but require budget planning. Pretrained multilingual models (e.g., Meta’s Massively Multilingual Speech) can accelerate development by leveraging transfer learning, but fine-tuning them still demands computational capacity. Additionally, tools like PyTorch or TensorFlow and libraries such as ESPnet or Fairseq are needed for implementation.
Finally, linguistic expertise ensures the model aligns with the language’s structure. This includes understanding phonetics, orthography, and prosody (stress, intonation). For example, languages with non-Latin scripts (e.g., Arabic or Devanagari) may require custom grapheme-to-phoneme rules. Collaborating with linguists or native speakers helps identify edge cases, such as rare phonemes or ambiguous pronunciations. Post-training evaluation—using metrics like Mean Opinion Score (MOS) or word error rate (WER)—and iterative refinement are critical. Deployment also requires infrastructure for scalability, such as APIs or on-device optimization for low-resource environments. Without these components, the model may struggle with naturalness or fail to handle real-world variability.