Human Parity in Speech Recognition

Established: December 1, 2015

This ongoing project aims to drive the state of the art in speech recognition toward  matching, and ultimately surpassing, humans, with a focus on unconstrained conversational speech.   The goal is a moving target as the scope of the task is broadened from high signal-to-noise speech between strangers (like in the Switchboard corpus) to include scenarios that make recognition more challenging, such as:  conversation among familiar speakers, multi-speaker meetings, and speech captured in noisy or distant-microphone environments.


DataSkeptic podcast interview on human versus machine transcription



Portrait of Wayne Xiong

Wayne Xiong

Principal Engineering Manager

Portrait of Xuedong Huang

Xuedong Huang

Technical Fellow and Chief Technology Officer Azure AI