Miao Lien Chen

US Patent:

20130158982, Jun 20, 2013

Filed:

Nov 29, 2012

Appl. No.:

13/688306

Inventors:

Educational Testing Service - Princeton NJ, US
Keelan Evanini - Pennington NJ, US
Lei Chen - Pennington NJ, US
Shasha Xie - Lawrenceville NJ, US
Wenting Xiong - Pittsburgh PA, US
Fei Huang - Philadelphia PA, US
Jana Sukkarieh - Princeton NJ, US
Miao Chen - Bloomington IN, US

Assignee:

EDUCATIONAL TESTING SERVICE - Princeton NJ

International Classification:

G06F 17/28

US Classification:

704 9

Abstract:

Systems and methods are provided for scoring a non-scripted speech sample. A system includes one or more data processors and one or more computer-readable mediums. The computer-readable mediums are encoded with a non-scripted speech sample data structure, where the non-scripted speech sample data structure includes: a speech sample identifier that identifies a non-scripted speech sample, a content feature extracted from the non-scripted speech sample, and a content-based speech score for the non-scripted speech sample. The computer-readable mediums further include instructions for commanding the one or more data processors to extract the content feature from a set of words automatically recognized in the non-scripted speech sample and to score the non-scripted speech sample by providing the extracted content feature to a scoring model to generate the content-based speech score.

US Patent:

20110213610, Sep 1, 2011

Filed:

Feb 25, 2011

Appl. No.:

13/035428

Inventors:

Lei Chen - Lawrenceville NJ, US
Joel Tetreault - Lawrenceville NJ, US
Xiaoming Xi - Pennington NJ, US
Klaus Zechner - Princeton NJ, US
Miao Chen - Bloomington IN, US

International Classification:

G06F 17/27

US Classification:

704 9, 704E11001

Abstract:

Systems and methods are provided for providing a score for a spontaneous non-native speech response to a prompt. A transcription of the spontaneous speech response is accessed. A plurality of clauses are identified within the spontaneous speech response, where identifying a clause includes identifying a beginning boundary and an end boundary of the clause in the spontaneous speech response. A plurality of disfluencies in the spontaneous speech response is identified. One or more proficiency metrics are calculated based on the plurality of identified clauses and the plurality of the identified disfluencies, and a score for the spontaneous speech response is generated based on the one or more proficiency metrics.