Title : Towards Principled Methods for Training Generative Adversarial Networks
Speaker : Martin Arjovsky , PhD student at New York University 
Date : 31st January 2017
Time : 7pm
Venue: KD101

Abstract: 
We have recently seen a couple of breakthroughs in speech
recognition – Microsoft’s systems have reached human parity in
transcribing speech and Baidu’s system is said to be 3 times faster than
human transcription for typing text messages, and just as accurate as
humans. Does this mean that the problem is solved? How did Deep Learning
manage to revolutionize the field? What does it take to replicate this
success in new languages and domains? In this talk, I will present the
problem of Automatic Speech Recognition and talk about the various Machine
Learning solutions that have been proposed over the years. We will look at
research and results on specific datasets and trace the improvement of
speech recognition systems on them. I will also present some
state-of-the-art Deep Learning models that are being used for ASR and
discuss some open problems in the field.

Referneces

[1]: Towards Principled Methods for Training Generative Adversarial Networks
[2]: Wasserstein GANs (arxiv link to arrive on Monday)

P.S. - If you have time, please read the paper[1].

Speaker Bio: Sunayana is currently a Post Doctoral researcher at Microsoft
Research India working on speech and language technologies for
multilingual communities. Her current focus is on building automatic
speech recognizers for code switched speech or mixed-language speech.
Previously, She completed her Masters and PhD in the School of Computer
Science at Carnegie Mellon University, and her dissertation was on speech
synthesis for low resource languages, advised by Prof Alan Black. My
research interests span speech and language technologies, machine learning
and intelligent agents. Refer to her webpage :
https://www.cs.cmu.edu/~ssitaram/