How speech models fail where it matters the most and what to do about it

Together AI · 4 months ago

Speech recognition systems achieve an average 39% transcription error rate on street names from diverse speakers, with an 18% accuracy gap between non-English and English primary speakers. The researchers reduced these errors by up to 60% using cross-lingual style transfer on fewer than 1,000 synthetic training samples. These improvements address a critical gap where street name errors in navigation and emergency dispatch systems cause significant delays and economic losses, particularly affecting non-English speakers.