Woojay Jeon from 4834 Clarendon Dr, San Jose, CA 95129, age 48

Resumes

Machine Learning Engineer

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Location:

4834 Clarendon Dr, San Jose, CA 95129

Industry:

Computer Software

Work:

Samsung Electronics - Suwon, South Korea since 2011
Senior Engineer
Motorola Nov 2008 - 2011
Senior Staff Engineer
Motorola Dec 2006 - Nov 2008
Senior Electrical Engineer
Georgia Institute of Technology Oct 2000 - Aug 2006
Graduate Research Assistant, Center for Signal and Image Processing
Motorola Labs May 2005 - Aug 2005
Intern

Education:

Georgia Institute of Technology 2002 - 2006
Georgia Institute of Technology 2000 - 2002
Seoul National University 1995 - 1999

Skills:

Machine Learning
Signal Processing
Algorithms
Pattern Recognition
Python
Speech Recognition
Natural Language Processing
Statistical Modeling
C++
Matlab
Digital Signal Processors
Mobile Devices
Linux
Digital Signal Processing
Programming
Embedded Software
Perl
Information Retrieval

Languages:

Korean
English

Woojay Jeon

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Publications

Us Patents

Automatic Pattern Recognition Using Category Dependent Feature Selection

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US Patent:

8380506, Feb 19, 2013

Filed:

Nov 29, 2007

Appl. No.:

11/998262

Inventors:

Woojay Jeon - Schaumburg IL, US

Assignee:

Georgia Tech Research Corporation - Atlanta GA

International Classification:

G10L 15/04

US Classification:

704254, 704231

Abstract:

Disclosed are apparatus and methods that employ a modified version of a computational model of the human peripheral and central auditory system, and that provide for automatic pattern recognition using category dependent feature selection. The validity of the output of the model is examined by deriving feature vectors from the dimension expanded cortical response of the central auditory system for use in a conventional phoneme recognition task. In addition, the cortical response may be a place-coded data set where sounds are categorized according to the regions containing their most distinguishing features. This provides for a novel category-dependent feature selection apparatus and methods in which this mechanism may be utilized to better simulate robust human pattern (speech) recognition.

Methods For Creating And Searching A Database Of Speakers

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US Patent:

8442823, May 14, 2013

Filed:

Oct 19, 2010

Appl. No.:

12/907729

Inventors:

Woojay Jeon - Chicago IL, US
Yan-Ming Cheng - Inverness IL, US
Changxue Ma - Barrington IL, US
Dusan Macho - Arlington Heights IL, US

Assignee:

Motorola Solutions, Inc. - Schaumburg IL

International Classification:

G10L 15/00

US Classification:

704246

Abstract:

A method of performing a search of a database of speakers, includes: receiving a query speech sample spoken by a query speaker; deriving a query utterance from the query speech sample; extracting query utterance statistics from the query utterance; performing Kernelized Locality-Sensitive Hashing (KLSH) using a kernel function, the KLSH using as input the query utterance statistics and utterance statistics extracted from a plurality of utterances included in a database of speakers in order to select a subset of the plurality of utterances; and comparing, using an utterance comparison equation, the query utterance statistics to the utterance statistics for each utterance in the subset to generate a list of speakers from the database of utterances having a highest similarity to the query speaker.

Method And Apparatus For Best Matching An Audible Query To A Set Of Audible Targets

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US Patent:

20110154977, Jun 30, 2011

Filed:

Dec 30, 2009

Appl. No.:

12/649458

Inventors:

Woojay Jeon - Chicago IL, US
Changxue Ma - Barrington IL, US

Assignee:

MOTOROLA, INC. - Schaumburg IL

International Classification:

G10H 7/00

US Classification:

84609

Abstract:

During operation, a “coarse search” stage applies variable-scale windowing on the query pitch contours to compare them with fixed-length segments of target pitch contours to find matching candidates while efficiently scanning over variable tempo differences and target locations. Because the target segments are of fixed-length, this has the effect of drastically reducing the storage space required in a prior-art method. Furthermore, by breaking the query contours into parts, rhythmic inconsistencies can be more flexibly handled. Normalization is also applied to the contours to allow comparisons independent of differences in musical key. In a “fine search” stage, a “segmental” dynamic time warping (DTW) method is applied that calculates a more accurate similarity score between the query and each candidate target with more explicit consideration toward rhythmic inconsistencies.

Unit-Selection Text-To-Speech Synthesis Using Concatenation-Sensitive Neural Networks

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US Patent:

20170092259, Mar 30, 2017

Filed:

Dec 7, 2015

Appl. No.:

14/961370

Inventors:

- Cupertino CA, US
Woojay JEON - Cupertino CA, US

International Classification:

G10L 13/07
G10L 13/047
G10L 13/08

Abstract:

Systems and processes for performing unit-selection text-to-speech synthesis are provided. In one example process, a sequence of target units can represent a spoken pronunciation of text. A set of predicted acoustic model parameters of a second target unit can be determined using a set of acoustic features of a first candidate speech segment of a first target unit and a set of linguistic features of the second target unit. A likelihood score of the second candidate speech segment with respect to the first candidate speech segment can be determined using the set of predicted acoustic model parameters of the second target unit and a set of acoustic features of the second candidate speech segment of the second target unit. The second candidate speech segment can be selected for speech synthesis based on the determined likelihood score. Speech corresponding to the received text can be generated using the selected second candidate speech segment.

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