Most people take for granted that when they speak, they will be heard and understood. But for the millions who live with speech impairments caused by physical or neurological conditions, trying to communicate with others can be difficult and lead to frustration. While there have been a great number of recent advances in automatic speech recognition (ASR; a.k.a. speech-to-text) technologies, these interfaces can be inaccessible for those with speech impairments. Further, applications that rely on speech recognition as input for text-to-speech synthesis (TTS) can exhibit word substitution, deletion, and insertion errors. Critically, in today’s technological environment, limited access to speech interfaces, such as digital assistants that depend on directly understanding one’s speech, means being excluded from state-of-the-art tools and experiences, widening the gap between what those with and without speech impairments can access.
The Columbia Engineering community has come together to combat the coronavirus pandemic on multiple fronts. In close collabo-ration with the Columbia University Irving Medical Center, we’re leveraging our expertise and innovation to address short term medical needs and long term societal impacts.
Building Agile Machine Learning Models
Monday 4:00 pm
Register here - https://www.eventbrite.com/e/building-agile-machine-learning-models-tickets-152359293749
Richard Zemel, University of Toronto