Poster Title

The TELLAR Model for Speech Recognition

Research Mentor(s)

Brian Hutchinson

Affiliated Department

Computer Sciences

Sort Order

51

Start Date

14-5-2015 10:00 AM

End Date

14-5-2015 2:00 PM

Document Type

Event

Abstract

The "TEnsor Log-LineAR" (TELLAR) model probabilistically maps acoustics to speech sounds (phones), a key step in speech recognition systems. This model uses a low n-rank tensor to perform this mapping, and in doing so, finds linear transforms of acoustics and phones into low dimensional spaces. By embedding phones into a low dimensional space, the model is capable of pooling information about related speech sounds, and is able to make better predictions with less data. It also aids interpretability: similar phones will be clustered near to each other in this space. Training the model involves solving a non-smooth convex optimization problem, for which we have an efficient algorithm and the guarantee of finding a globally optimal solution. Initial results in phone classification are promising, but this work is on-going. Next, we plan to incorporate the TELLAR model into state of the art speech recognition systems to improve their performance.

This document is currently not available here.

Share

COinS
 
May 14th, 10:00 AM May 14th, 2:00 PM

The TELLAR Model for Speech Recognition

Computer Sciences

The "TEnsor Log-LineAR" (TELLAR) model probabilistically maps acoustics to speech sounds (phones), a key step in speech recognition systems. This model uses a low n-rank tensor to perform this mapping, and in doing so, finds linear transforms of acoustics and phones into low dimensional spaces. By embedding phones into a low dimensional space, the model is capable of pooling information about related speech sounds, and is able to make better predictions with less data. It also aids interpretability: similar phones will be clustered near to each other in this space. Training the model involves solving a non-smooth convex optimization problem, for which we have an efficient algorithm and the guarantee of finding a globally optimal solution. Initial results in phone classification are promising, but this work is on-going. Next, we plan to incorporate the TELLAR model into state of the art speech recognition systems to improve their performance.