Unverified60% confidenceFactExact time
The introduction of deep neural networks in 2012 caused speech recognition error rates to drop by more than 30%.
2
Sources
60%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
UnverifiedThe latest version of pyannote.audio adopts an end-to-end neural network architecture (EEND), and the diarization error rate (DER) on standard evaluation sets has dropped below 10%.73% similarUnverified有研究表明在自发语音场景下WER从5%提升到8%，用户感知的任务完成率可能下降超过30%69% similarUnverified现代语音识别技术基于深度学习中的端到端神经网络架构，尤其是Transformer模型的引入使识别准确率大幅提升66% similarUnverifiedModern speech transcription systems are mostly based on end-to-end deep learning architectures such as Whisper and Conformer, and their accuracy is already quite high in quiet environments, but in technical interview scenarios, specialized terminology (such as Kubernetes, Dijkstra), English abbreviations, accents and changes in speaking rate may cause recognition errors.65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/521177API
curl https://kongchang.com/api/v1/knowledge/claims/521177MCP
get_claim(id=521177)