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Kochem, Tim; Beck, Jeanne; Goodale, Erik – CALICO Journal, 2022
Technology has paved the way for new modalities in language learning, teaching, and assessment. However, there is still a great deal of work to be done to develop such tools for oral communication, specifically tools that address suprasegmental features in pronunciation instruction. Therefore, this critical literature review examines how…
Descriptors: Computer Software, Teaching Methods, Audio Equipment, Computer Assisted Instruction
Yuko Hayashi; Yusuke Kondo; Yutaka Ishii – Innovation in Language Learning and Teaching, 2024
Purpose: This study builds a new system for automatically assessing learners' speech elicited from an oral discourse completion task (DCT), and evaluates the prediction capability of the system with a view to better understanding factors deemed influential in predicting speaking proficiency scores and the pedagogical implications of the system.…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Japanese
Cox, Troy L.; Brown, Alan V.; Thompson, Gregory L. – Language Testing, 2023
The rating of proficiency tests that use the Inter-agency Roundtable (ILR) and American Council on the Teaching of Foreign Languages (ACTFL) guidelines claims that each major level is based on hierarchal linguistic functions that require mastery of multidimensional traits in such a way that each level subsumes the levels beneath it. These…
Descriptors: Oral Language, Language Fluency, Scoring, Cues
Jones, Daniel Marc; Cheng, Liying; Tweedie, M. Gregory – Canadian Journal of Learning and Technology, 2022
This article reviews recent literature (2011-present) on the automated scoring (AS) of writing and speaking. Its purpose is to first survey the current research on automated scoring of language, then highlight how automated scoring impacts the present and future of assessment, teaching, and learning. The article begins by outlining the general…
Descriptors: Automation, Computer Assisted Testing, Scoring, Writing (Composition)
Daniels, Paul – TESL-EJ, 2022
This paper compares the speaking scores generated by two online systems that are designed to automatically grade student speech and provide personalized speaking feedback in an EFL context. The first system, "Speech Assessment for Moodle" ("SAM"), is an open-source solution developed by the author that makes use of Google's…
Descriptors: Speech Communication, Auditory Perception, Computer Uses in Education, Computer Assisted Testing
Konopka, Agnieszka E. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2019
Two experiments tracked the encoding of relational information (actions at the level of the prelinguistic message and verbs at the level of the sentence) during formulation of transitive event descriptions (e.g., The tiger is scratching the photographer). At what point during message and sentence formulation do speakers encode actions and verbs?…
Descriptors: Verbs, Language Processing, Psycholinguistics, Sentences
Miyamoto, Mayu – ProQuest LLC, 2019
Despite an emphasis on oral communication in most foreign language classrooms, the resource-intensive nature (i.e. time and manpower) of speaking tests hinder regular oral assessments. A possible solution is the development of a (semi-) automated scoring system. When it is used in conjunction with human raters, the consistency of computers can…
Descriptors: Second Language Learning, Speech Communication, Oral Language, Foreign Countries
Linlin, Cao – English Language Teaching, 2020
Through Many-Facet Rasch analysis, this study explores the rating differences between 1 computer automatic rater and 5 expert teacher raters on scoring 119 students in a computerized English listening-speaking test. Results indicate that both automatic and the teacher raters demonstrate good inter-rater reliability, though the automatic rater…
Descriptors: Language Tests, Computer Assisted Testing, English (Second Language), Second Language Learning
Jiao, Yishan; LaCross, Amy; Berisha, Visar; Liss, Julie – Journal of Speech, Language, and Hearing Research, 2019
Purpose: Subjective speech intelligibility assessment is often preferred over more objective approaches that rely on transcript scoring. This is, in part, because of the intensive manual labor associated with extracting objective metrics from transcribed speech. In this study, we propose an automated approach for scoring transcripts that provides…
Descriptors: Suprasegmentals, Phonemes, Error Patterns, Scoring
Hannah, L.; Kim, H.; Jang, E. E. – Language Assessment Quarterly, 2022
As a branch of artificial intelligence, automated speech recognition (ASR) technology is increasingly used to detect speech, process it to text, and derive the meaning of natural language for various learning and assessment purposes. ASR inaccuracy may pose serious threats to valid score interpretations and fair score use for all when it is…
Descriptors: Task Analysis, Artificial Intelligence, Speech Communication, Audio Equipment
Ashwell, Tim; Elam, Jesse R. – JALT CALL Journal, 2017
The ultimate aim of our research project was to use the Google Web Speech API to automate scoring of elicited imitation (EI) tests. However, in order to achieve this goal, we had to take a number of preparatory steps. We needed to assess how accurate this speech recognition tool is in recognizing native speakers' production of the test items; we…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Language Tests
Gelfand, Stanley A.; Gelfand, Jessica T. – Journal of Speech, Language, and Hearing Research, 2012
Method: Complete psychometric functions for phoneme and word recognition scores at 8 signal-to-noise ratios from -15 dB to 20 dB were generated for the first 10, 20, and 25, as well as all 50, three-word presentations of the Tri-Word or Computer Assisted Speech Recognition Assessment (CASRA) Test (Gelfand, 1998) based on the results of 12…
Descriptors: Scoring, Word Recognition, Young Adults, Phonemes
Bridgeman, Brent; Powers, Donald; Stone, Elizabeth; Mollaun, Pamela – Language Testing, 2012
Scores assigned by trained raters and by an automated scoring system (SpeechRater[TM]) on the speaking section of the TOEFL iBT[TM] were validated against a communicative competence criterion. Specifically, a sample of 555 undergraduate students listened to speech samples from 184 examinees who took the Test of English as a Foreign Language…
Descriptors: Undergraduate Students, Speech Communication, Rating Scales, Scoring

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