[Colloquium] REMINDER: 4/9 TTIC Colloquium: Rohit Prabhavalkar, Google

[Mary Marre via Colloquium]

 When:     Monday, April 9th at *10:30 am*

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

Who:        Rohit Prabhavalkar, Google


Title:        End-to-End Modeling For Automatic Speech Recognition

Abstract: Traditional approaches to automatic speech recognition (ASR) rely
on separate acoustic model (AM), pronunciation model (PM) and language
model (LM) components. Typically, these modules are either trained
independently, or are curated using expert knowledge. Over the last several
years, there has been a growing interest in developing "end-to-end" speech
recognition systems which attempt to learn all of these components jointly
in a single system. In this talk, I shall discuss our work involving various
algorithmic and modeling improvements  to build end-to-end speech
recognition systems which surpass the performance of a conventional ASR
system. I shall also discuss promising results obtained by applying this
approach to the task of multi-lingual and multi-dialect speech recognition.
Finally, I shall discuss some of the current challenges with these models
and outline future research directions.



Host: Karen Livescu <klivescu at ttic.edu>



For more information on the colloquium series or to subscribe to the
mailing list,please see http://www.ttic.edu/colloquium.php




Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Mon, Apr 2, 2018 at 4:41 PM, Mary Marre <mmarre at ttic.edu> wrote:

> When:     Monday, April 9th at *10:30 am*
>
> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>
> Who:        Rohit Prabhavalkar, Google
>
>
> Title:        End-to-End Modeling For Automatic Speech Recognition
>
> Abstract: Traditional approaches to automatic speech recognition (ASR)
> rely on separate acoustic model (AM), pronunciation model (PM) and language
> model (LM) components. Typically, these modules are either trained
> independently, or are curated using expert knowledge. Over the last several
> years, there has been a growing interest in developing "end-to-end" speech
> recognition systems which attempt to learn all of these components jointly
> in a single system. In this talk, I shall discuss our work involving various
> algorithmic and modeling improvements  to build end-to-end speech
> recognition systems which surpass the performance of a conventional ASR
> system. I shall also discuss promising results obtained by applying this
> approach to the task of multi-lingual and multi-dialect speech recognition.
> Finally, I shall discuss some of the current challenges with these models
> and outline future research directions.
>
>
>
> Host: Karen Livescu <klivescu at ttic.edu>
>
>
>
> For more information on the colloquium series or to subscribe to the
> mailing list,please see http://www.ttic.edu/colloquium.php
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757*
> *f: (773) 357-6970*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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