The Role of Datasets in Text-to-Speech Technology

Swift development of artificial intelligence has being pushing revolutions in all walks of life, and the function of data is crucial. In the training process of AI models, high-quality datasets are like fuel, directly determines the performance and accuracy of the algorithm. With demand soaring for intelligence, various datasets have gradually become core resources for research and application.

Text-to-speech (TTS) or speech synthesis technology has made remarkable strides in recent years, revolutionizing the way humans interact with computers and digital devices. This cutting-edge technology converts written text into natural-sounding speech, enabling applications like voice assistants, audiobooks, and accessibility tools. The development of high-quality TTS systems heavily relies on the availability and quality of datasets used for training the models.

Creating a high-quality TTS dataset is a meticulous process that involves multiple stages. Firstly, large amounts of speech data are collected from various sources, including public domain recordings, audiobooks, and crowd-sourced contributions. This diverse dataset captures the richness of linguistic variations and accents, ensuring that the synthesized speech is inclusive and caters to a wide range of users.

Once the raw speech data is collected, it undergoes a rigorous cleaning process to remove any background noise or disturbances. The data is then meticulously annotated, aligning the corresponding text with the speech segments. These annotations are essential for training the TTS models as they provide the necessary information for the system to learn the relationship between text and speech.

In the globalized world we live in, multilingual capabilities are a fundamental requirement for TTS systems. Multilingual datasets are invaluable for training models to accurately synthesize speech in multiple languages. These datasets introduce the TTS model to the phonetic and linguistic peculiarities of various languages, enhancing its adaptability and usability.

Nexdata Text-to-Speech Datasets

19.46 Hours - American English Speech Synthesis Corpus-Female

Female audio data of American English,. It is recorded by American English native speaker, with authentic accent and sweet sound. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.

20 Hours - American English Speech Synthesis Corpus-Male

Male audio data of American English. It is recorded by American English native speakers, with authentic accent. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.

10.4 Hours - Japanese Synthesis Corpus-Female

It is recorded by Japanese native speaker, with authentic accent. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.

22 People - Chinese Mandarin Multi-emotional Synthesis Corpus

22 People - Chinese Mandarin Multi-emotional Synthesis Corpus. It is recorded by Chinese native speaker, covering different ages and genders. six emotional text, and the syllables, phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.

The future intelligent system will increasingly rely on high-quality datasets to optimize decision-making and automated processes. In the era of data, companies and researchers need to continuously improve their ability of data collection and annotation to make sure the efficiency and accuracy of AI models. To gain an advantageous position in fiercely competitive market, we must laid a solid foundation in data.