Overcoming Data Challenges in Neural Machine Translation

During the epidemic, telecommuting and off-site work have become hot words, and the demand for online work has continued to increase. Meeting forms such as video conferences and remote conferences have become important scenarios for internal communication in enterprises. Now, it seems like every business meeting uses Zoom, Google Meet, or Microsoft Teams.

According to the report, the overall market size of China’s video conferencing in 2020 is 8.1 billion Yuan, of which the emerging video conferencing market is 2.96 billion Yuan, an increase of 174.1% over the previous year. At the same time, the stock of video conferencing-related companies is showing a clear upward trend. In 2021, China has more than 500,000 video conference-related companies, and in 2022, this figure has increased by as much as 500,000.

Conference scenarios have strong demand for voice-to-text products. However, due to technical limitations, traditional voice technology have problems such as “inaccurate hearing, indistinguishability, and incomprehension”, often with low recognition accuracy under noise, unable to distinguish different speakers, and the transcribed content is long and difficult to read and precipitate.

As the world’s leading AI data service provider, Nexdata has been committed to breaking the technical bottleneck and using high-quality data services to help customers improve their AI models, to create a more intelligent and humane conference experience.

Nexdata provides on-demand speech data collection & annotation services. Nexdata has professional data collection equipment, tools and environment. Our project managers have extensive experiences in data collection and quality control. We can meet various scenarios and types of speech data collection needs. As for speech data annotation, Nexdata has 3 mega-data bases and more than 5,000 professional annotators, supporting multiple data annotation services, such as speech, image, video, point cloud and text, etc.

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.