Automated Scoring of Speech

ETS's SpeechRater℠ engine is the only spoken response scoring application that is used to score spontaneous responses, in which the range of valid responses is open-ended rather than narrowly determined by the item stimulus. Test takers preparing to take the TOEFL® test have had their responses scored by the SpeechRater engine as part of the TOEFL Practice Online test since 2006. Competing capabilities focus on assessing low-level aspects of speech production such as pronunciation by using restricted tasks in order to increase reliability. The SpeechRater engine, by contrast, is based on a broad conception of the construct of English speaking proficiency, encompassing aspects of speech delivery (such as pronunciation and fluency), grammatical facility and higher-level abilities related to topical coherence and the progression of ideas.

The SpeechRater engine processes each response with an automated speech recognition system specially adapted for use with nonnative English. Based on the output of this system, natural language processing is used to calculate a set of features that define a "profile" of the speech on a number of linguistic dimensions, including fluency, pronunciation, vocabulary usage and prosody. A model of speaking proficiency is then applied to these features in order to assign a final score to the response. While the structure of this model is informed by content experts, it is also trained on a database of previously observed responses scored by human raters, in order to ensure that SpeechRater's scoring emulates human scoring as closely as possible. Furthermore, if the response is found to be unscorable due to audio quality or other issues, the SpeechRater engine can set it aside for special processing.

ETS's research agenda related to automated scoring of speech includes the development of more extensive Natural Language Processing (NLP) features to represent grammatical competencies and the discourse structure of spoken responses. The core capability is also being extended to apply across a range of item types used in different assessments of English proficiency, including a range of options from very restricted item types (such as passage read-alouds), through less restrictive items (such as summarization tasks), to fully open-ended items.

Featured Publications

Below are some recent or significant publications that our researchers have authored on the subject of automated scoring of speech.



  • Performance of a Trialogue-based Prototype System for English Language Assessment for Young Learners
    K. Evanini, Y. So, J. Tao, D. Zapata-Rivera, C. Luce, L. Battistini, & X. Wang
    Paper in Proceedings of the Interspeech Workshop on Child Computer Interaction (WOCCI 2014)

    This paper describes a trialogue-based system for assessing the spoken language abilities of young learners of English. Specifically, the system employs spoken dialogue system components in interactive, conversation-based assessment tasks involving the test taker and two virtual interlocutors. View paper >

  • Automatic Detection of Plagiarized Spoken Responses
    K. Evanini & X. Wang
    Paper in Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications pp. 22–27

    This paper addresses the task of automatically detecting plagiarized responses in the context of a test of spoken English proficiency for nonnative speakers. A corpus of spoken responses containing plagiarized content was collected from a high-stakes assessment of English proficiency for nonnative speakers. View paper >

  • Similarity-Based Non-Scorable Response Detection for Automated Speech Scoring
    S. Y. Yoon & S. Xie
    Paper in Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 116–123

    This paper describes a method that filters out spoken responses from the test takers who try to game the system using diverse strategies such as speaking in their native languages or by citing memorized responses for unrelated topics. View citation record >



  • A Comparison of Two Scoring Methods for an Automated Speech Scoring System
    X. Xi, D. Higgins, K. Zechner, & D. Williamson
    Language Testing, Vol. 29, No. 3, pp. 371–394

    In this paper, researchers compare two alternative scoring methods for an automated scoring system for speech. The authors discuss tradeoffs between multiple regression and classification tree models. View citation record >

  • Exploring Content Features for Automated Speech Scoring
    S. Xie, K. Evanini, & K. Zechner
    Proceedings of the 2012 Meeting of the North American Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)
    Association for Computational Linguistics

    Researchers explore content features for automated speech scoring in this paper about automated scoring of unrestricted spontaneous speech. The paper compares content features based on three similarity measures in order to understand how well content features represent the accuracy of the content of a spoken response. View citation record >

  • Assessment of ESL Learners' Syntactic Competence Based on Similarity Measures
    S. Yoon & S. Bhat
    Paper in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

    In this paper, researchers present a method that measures English language learners' syntactic competence for the automated speech scoring systems. The authors discuss the advantage of the current natural language processing technique-based and corpus-based measures over the conventional ELL measures. View citation record >



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