Advancing Text-to-Speech Systems for Low-Resource Languages: Challenges, Innovations, and Future Directions

Speech synthesis, the technology that converts text into spoken words, has advanced significantly for high-resource languages like English, Spanish, and Mandarin. However, many languages spoken by millions of people are still underserved by speech synthesis systems due to insufficient data to train accurate models. An analysis of current solutions and difficulties in low-resource language speech synthesis development is presented in this paper because these languages have restricted data and linguistic resources. The paper examines different research strategies for advancing speech synthesis techniques in these languages by borrowing data from rich-resource languages to enhance models used in poor-resource systems. The primary method involves the comparison of linguistic models that operate between highly supported languages to adapt them to languages that only have limited information available. Acoustic models requiring advancement represent a crucial need to process correctly the phonetic and prosodic attributes within these languages because their traits lack proper representation in small data collections. The paper investigates different data augmentation methods, which include synthetic data generation and multilingual corpus use to enhance available training resources. These methods enhance the quality of computer-generated speech by making it sound more natural while ensuring that the words remain clear and understandable, even in low-resource languages. Notably, data augmentation techniques expanded dataset sizes by up to 27 times, improving model robustness. Additionally, models like WaveNet achieved a Mean Opinion Score (MOS) of 4.21, while Tacotron-based systems reached MOS scores as high as 4.38, demonstrating high naturalness even with limited training data. Experiments using less than three hours of training data in low-resource settings initially showed Word Error Rates (WER) exceeding 50–80%, but data augmentation and transfer learning significantly reduced these error rates. By reviewing the present state of research, this paper offers an overview of the progress made in low-resource language speech synthesis. It identifies the potential future directions for this field. Ultimately, the goal is to make speech synthesis more accessible for underrepresented languages, helping with language preservation, assistive technologies, and broader accessibility.