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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.
Datatang Text-to-Speech Datasets
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.
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.
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. 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.
In the realm of technological advancement, speech recognition has emerged as a groundbreaking innovation that has revolutionized human-computer interaction. From voice assistants to transcription services, this technology has become an indispensable part of our daily lives. However, one of the significant challenges in this domain lies in accurately recognizing and processing the Malay language, a complex and diverse language with unique linguistic features.
A parallel corpus is a collection of texts in two or more languages that are aligned at a sentence or phrase level, allowing a direct comparison between the languages. Essentially, it is a linguistic goldmine containing translations of the same content in multiple languages. These translations can range from literary works and legal documents to scientific articles and everyday conversations.