Open AccessAutomated Scoring of Nonnative Speech Using the SpeechRater SM v. 5.0 Engine
Author(s) -
Chen Lei,
Zechner Klaus,
Yoon SuYoun,
Evanini Keelan,
Wang Xinhao,
Loukina Anastassia,
Tao Jidong,
Davis Lawrence,
Lee Chong Min,
Ma Min,
Mundkowsky Robert,
Lu Chi,
Leong Chee Wee,
Gyawali Binod
Publication year - 2018
Publication title -
ets research report series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.235
H-Index - 5
ISSN - 2330-8516
DOI - 10.1002/ets2.12198
Subject(s) - computer science , flagging , pronunciation , natural language processing , vocabulary , feature (linguistics) , service (business) , artificial intelligence , grammar , feature selection , prosody , speech recognition , linguistics , philosophy , economy , archaeology , economics , history
This research report provides an overview of the R&D efforts at Educational Testing Service related to its capability for automated scoring of nonnative spontaneous speech with the SpeechRater SM automated scoring service since its initial version was deployed in 2006. While most aspects of this R&D work have been published in various venues in recent years, no comprehensive account of the current state of SpeechRater has been provided since the initial publications following its first operational use in 2006. After a brief review of recent related work by other institutions, we summarize the main features and feature classes that have been developed and introduced into SpeechRater in the past 10 years, including features measuring aspects of pronunciation, prosody, vocabulary, grammar, content, and discourse. Furthermore, new types of filtering models for flagging nonscorable spoken responses are described, as is our new hybrid way of building linear regression scoring models with improved feature selection. Finally, empirical results for SpeechRater 5.0 (operationally deployed in 2016) are provided.