The recent resurgence of deep neural networks has sparked questions in classical statistical inference methods. A particular inference problem is dialect recognition, which alongside international communication has become increasingly important in recent times. My work utilizes semi-supervised learning to harness large amounts of unlabeled data and explores the strengths of novel network architectures including long-short term memory networks to improve accuracy in dialect recognition. Using standard techniques I also investigate methods to train these deep network architectures with a limited amount of data. Throughout my project I will extend DNN toolkits and kaldi recipes for studying speech recognition problems in support of the Spoken Language Systems Groups work.

I participated in the Jelinek Speech and Language Technology Workshop with a team developing an automatic speech recognition system trained exclusively from transcripts written by non-native speakers. My research interests lie in how to develop systems machines can utilize to ease their interactions with humans. I am excited to further the goal of allowing humans and machines to seamlessly communicate through speech.