TTSään

TTSään is a project that focuses on developing and improving text-to-speech (TTS) technology, particularly for under-resourced languages. The initiative aims to make TTS more accessible and functional for a wider range of linguistic communities by creating high-quality speech synthesis models. TTSään often involves research into natural language processing, phonetics, and machine learning to achieve more human-like and intelligible synthesized speech.

The project typically involves several key stages, starting with data collection and preparation. This includes gathering large amounts of spoken language data and corresponding transcriptions for the target languages. These datasets are crucial for training sophisticated machine learning models. Following data collection, the project focuses on model development, often employing deep learning architectures such as recurrent neural networks (RNNs) or transformer models. These models learn the complex relationships between text and speech sounds.

Evaluation is another vital component of TTSään. This involves assessing the quality, naturalness, and intelligibility of the generated speech through objective metrics and subjective listening tests. The feedback gathered from evaluation is then used to iterate and refine the models. TTSään's ultimate goal is to empower communities with advanced TTS capabilities, facilitating applications in areas like education, assistive technologies, and digital content creation for languages that may not have extensive commercial TTS support. The project emphasizes collaborative efforts and open-source contributions to accelerate progress in the field.