[Colloquium] TTIC Colloquium: Erik McDermott, Google

[Dawn Ellis]

When:     Thursday, August 28th at 11am

Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room #526

Who:       Erik McDermott, Google

Title:       Asynchronous Stochastic Optimization for Sequence Training of

              Deep Neural Networks: Towards Big Data

Abstract:

Previous work presented a proof of concept for sequence training of
deep neural networks (DNNs) using asynchronous stochastic
optimization, mainly focusing on a small-scale task. The approach
offers the potential to leverage both the efficiency of stochastic
gradient descent and the scalability of parallel computation. This
study presents results for four different voice search tasks to
confirm the effectiveness and efficiency of the proposed framework
across different conditions: amount of data (from 60 hours to 20,000
hours), type of speech (read speech vs. spontaneous speech), quality
of data (supervised vs. unsupervised data), and language.  Significant
gains over baselines (DNNs trained at the frame level) are found to
hold across these conditions. The experimental results are analyzed,
and additional practical details for the approach are
provided. Furthermore, different sequence training criteria are
compared.


Host:  Karen Livescu,  klivescu at ttic.edu

-- 
*Dawn Ellis*
Administrative Coordinator,
Bookkeeper
773-834-1757
dellis at ttic.edu

TTIC
6045 S. Kenwood Ave.
Chicago, IL. 60637
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