The Role of Speech Recognition Datasets in the Infinite Advancement of Voice-Based AI

Voice-based AI services are drawing attention as the most natural interface and a key portal for platform expansion. At the center of this technological advancement lies the benchmark dataset, which evaluates AI accuracy and sets the direction for the technology.

Speech recognition technology has evolved from open source such as Kaldi, through Hybrid Neural Architecture, to recent End-to-End models such as DeepSpeech, Encoder-decoder model, and Google's Transducer. In particular, performance measurements using datasets such as LibriSpeech show that the speech recognition error rate (WER) has already surpassed human-level performance since 2017.

In Korea as well, various speech recognition datasets are being built centered around AI Hub. In particular, data on customer service, in-vehicle commands, and dialects reflect real-world service requirements, giving them higher value compared to global data.

However, personal information protection issues that arise when using actual service data and the construction of large-scale untranscribed audio data for unsupervised learning remain challenges to be addressed going forward. Continued competitions and data-building efforts are expected to contribute to the advancement of Korean speech recognition technology.