Natural language research announcements

[Rajhans Samdani <rsamdan2 AT illinois.edu>]

Hi all,

This week we have Prof. Mark Hasegawa-Johnson 
(http://www.ifp.illinois.edu/~hasegawa/) from the ECE department as our 
speaker at 
AIIS. 

When: Friday, Feb 25, 2 pm.

Where: 3405, Siebel Center.

Title: Semi-Supervised Learning for Speech and Audio Processing

Abstract:
Semi-supervised learning requires one to make assumptions about the data.  
This talk 
will discuss three different assumptions, and algorithms that instantiate 
those 
assumptions, for the classification of speech and audio events.  First, I 
will discuss 
the case of phoneme classification, and the assumption of softly compact 
likelihood 
functions.  The acoustic spectra corresponding to different phonemes overlap 
each 
other willy-nilly, but at least there is a tendency for the instantiations of 
each 
phoneme to cluster within a well-defined region of the feature space---a sort 
of "soft 
compactness" assumption. Softly compact distributions can be learned by an 
algorithm that encourages compactness without strictly requiring it, e.g., by 
maximizing likelihood of the unlabeled data, or even better, by minimizing 
its 
conditional class entropy.  The resulting distributions are different from 
those that 
would be learned by a fully labeled dataset, demonstrating the "softness" of 
the 
compactness assumption. 
Second, I will discuss the problem of recognizing mispronounced words, and 
the 
assumption of softly compact transformed distributions.  In this problem we 
have not 
really developed a semi-supervised method, but rather a transformed method: 
the 
canonical phonetic pronunciations are transformed into an articulatory 
domain, 
possible mispronunciations are predicted based on a compactness criterion in 
the articulatory domain, and the result is transformed back into the phonetic 
domain, 
forming a rather bushy finite state transducer. 
Third, I will discuss the problems of audio normalization and unlabeled class 
discovery, and the assumption of softly compact distributions of 
distributions.  In this 
approach, we assume that each training token is generated by an "instance 
PDF" 
that is different from every other instance PDF, but that the instance PDFs 
corresponding to each labeled class are "softly compact" in the space of 
possible 
probability density functions.  Two methods based on this assumption are 
worth note.  
The GMM supervector method achieves excellent performance in tasks where the 
target labels are provided, but in which tokens are collected from unlabeled 
arbitrarily 
noisy environments.  Conversely, class discovery methods seek to generate 
labels for a class that does not exist in the labeled training set. 

See you all!
Best,
Rajhans


Rajhans Samdani,
Graduate Student,
Dept. of Computer Science,
University of Illinois at Urbana-Champaign.