Part 11/13:
This shift is crucial because the transcription step often introduces errors due to accents, background noise, or ambiguous phrasing. By focusing on understanding user intent directly, S2R improves accuracy and relevance, even across multiple languages and noisy environments. Tests across 17 languages show performance close to human comprehension, significantly outperforming previous transcription-based systems.
Google further supports this innovation with an open-source dataset called Simple Voice Questions, hosting a diverse range of recordings under different noise conditions to evaluate and improve the technology globally. This strategy aims to standardize sound-based AI systems and foster innovation outside proprietary ecosystems.