Schedule

Time TOPIC zoom link
3:45 p.m. Program Meeting for
Healthcare Systems Engineering
https://wse.zoom.us/j/99426784334
4 to 5 p.m. Program Meetings Applied and Computational Mathematics

Applied Biomedical Engineering

Applied Physics

Civil Engineering

Computer Science // Cybersecurity // (AI will be discussed the last 15 minutes of this program meeting)

Electrical and Computer Engineering

Engineering Management

Environmental Engineering, Science, and Management

Financial Mathematics

Information Systems Engineering

Materials Science and Engineering

Systems Engineering

5 to 5:15 p.m. Welcome and EP Update wse.zoom.us/j/92308550072
5:15 to 5:20 p.m. Faculty Affairs at EP wse.zoom.us/j/92308550072
5:20 to 5:25 p.m. Faculty Forward wse.zoom.us/j/92308550072
5:25 to 5:50 p.m. 2022 Faculty Awards Ceremony wse.zoom.us/j/92308550072
5:50 to 6:20 p.m. Keynote Address: Ariya Rastrow
“The All-Neural Era: Progress in Speech Recognition Technology at Alexa”
wse.zoom.us/j/92308550072
6:20 to 6:35 p.m. Keynote Q&A wse.zoom.us/j/92308550072

Keynote

Ariya Rastrow, senior principal scientist for Alexa AI.

“The All-Neural Era: Progress in Speech Recognition Technology at Alexa”
Speech recognition technology is undergoing a dramatic shift, moving to an all-neural architecture, replacing the conventional stack of disjointly trained neural and non-neural sub-systems. The unified all-neural architecture improves SOTA accuracy, while also achieving superior memory and compute compression, enabling streaming low-latency speech recognition at the edge, where resources are constrained. The unified all-neural architecture, in conjunction with multi-task learning approaches, overhauls the boundary between the spoken language understanding (SLU) sub-components of voice AI assistants, including end-pointing, speaker segmentation, device-directed classification, and language identification and language understanding, allowing for a jointly trained unified stack that goes beyond a transcription-only task. In this talk, I give an overview of the existing challenges and our recent progress in addressing different aspects of adapting all-neural technology for production ASR for Alexa, the AI assistant that serves millions of customers daily.

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