AI BriefingThe Role and Importance of Speech Recognition Datasets
Key point
This piece analyzes the advancement of speech recognition technology and the importance of benchmark datasets for measuring performance, comparing the status of domestic and international datasets.
Details
Voice-based AI services provide a natural interface and hold the potential to serve as a new portal through which each company can expand its platform. At the center of this technological advancement lie benchmark datasets, the common standard for measuring AI accuracy.
LibriSpeech was the most widely used benchmark dataset in the field of speech recognition from 2010 to 2020, and its trajectory aligns with the launch of AI services such as Siri and the emergence of major models including Kaldi, DeepSpeech, and Transducer.
Looking at WER (Word Error Rate), the performance metric for speech recognition, since 2017 the error rate on benchmark datasets has fallen below or surpassed Human Level performance. However, since these are results from benchmark environments, real-world performance may be lower than this.
In Korea, the dataset built by AI Hub at NIA (National Information Society Agency) is noteworthy. AI Hub offers high diversity and volume across various domains such as customer service, vehicle commands, meetings, lectures, and dialects, showing excellent performance even when compared to existing global datasets. However, personal information protection remains an issue to be resolved when applying it to actual services.
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