A Study of web-based oral activities enhanced by Automatic Speech Recognition for EFL college learning

Recently, a promising topic in computer-assisted language learning is the application of Automatic Speech Recognition (ASR) technology for assisting learners to engage in meaningful speech interactions. Simulated real-life conversation supported by the application of ASR has been suggested as helpful for speaking. In this study, a web-based conversation environment called CandleTalk, which allows learners to seemingly talk with the computer, was developed to help EFL learners receive explicit speech acts training that leads to better oral competence. CandleTalk is equipped with an ASR engine that judges whether learners provide appropriate input. Six speech acts are presented as the foci of the materials with local cultural information incorporated as the content of the dialogues to enhance student motivation. The materials were put to use on 29 English major and 20 non-English major students in order to investigate their learning outcome and perception in an EFL context. Oral proficiency assessment using the format of the Discourse Completion Test (DCT) given before and after the use of CandleTalk and an evaluation questionnaire were two instruments used for data collection. The results of the study showed that the application of ASR was helpful for the college freshmen in the teaching of speech acts, particularly for the non-English major students. Most learners perceived positively toward the instruction supported with speech recognition.

[1]  Debra M. Hardison,et al.  Generalization of Computer Assisted Prosody Training: Quantitative and Qualitative Findings , 2004 .

[2]  Amir Najmi,et al.  Subarashii: Encounters in Japanese Spoken Language Education , 1999 .

[3]  Stephen E. Levinson,et al.  Speech Recognition by Computer. , 1981 .

[4]  J. Sadock Speech acts , 2007 .

[5]  Patti Price,et al.  VILTS: A Tale of Two Technologies , 1999 .

[6]  Tracey M. Derwing,et al.  Processing Time, Accent, and Comprehensibility in the Perception of Native and Foreign-Accented Speech , 1995, Language and speech.

[7]  S. Blum-Kulka Learning to Say What You Mean in a Second Language: A Study of the Speech Act Performance of Learners of Hebrew as a Second Language1 , 1982 .

[8]  Jack Mostow,et al.  Giving Help and Praise in a Reading Tutor with Imperfect Listening--Because Automated Speech Recognition Means Never Being Able to Say You're Certain , 2013, CALICO Journal.

[9]  David Coniam,et al.  Voice Recognition Software Accuracy with Second Language Speakers of English. , 1999 .

[10]  G. Kasper,et al.  Cross-Cultural Pragmatics: Requests and Apologies , 1991 .

[11]  Kathleen B. Egan Speaking: A Critical Skill and a Challenge , 1999 .

[12]  A. Cohen,et al.  The Production of Speech Acts by EFL Learners , 1993 .

[13]  Farzad Ehsani,et al.  Speech Technology in Computer-Assisted Language Learning: Strengths and Limitations of a New CALL Paradigm. , 1998 .

[14]  Stephen A LaRocca,et al.  On the Path to 2X Learning: Exploring the Possibilities of Advanced Speech Recognition. , 1999 .

[15]  Maxine Eskenazi,et al.  USING AUTOMATIC SPEECH PROCESSING FOR FOREIGN LANGUAGE PRONUNCIATION TUTORING: SOME ISSUES AND A PROTOTYPE , 1999 .

[16]  Krystyna A. Wachowicz,et al.  Software That Listens: It's Not a Question of Whether, It's a Question of How , 1999 .

[17]  Robert C. Duncan,et al.  Virtual Dialogues with Native Speakers: The Evaluation of an Interactive Multimedia Method , 1999 .

[18]  G. Kartal,et al.  Working With an Imperfect Medium: Speech Recognition Technology in Reading Practice , 2006 .

[19]  Helmer Strik,et al.  The Pedagogy-Technology Interface in Computer Assisted Pronunciation Training , 2002 .

[20]  A. Cohen,et al.  DEVELOPING A MEASURE OF SOCIOCULTURAL COMPETENCE: THE CASE OF APOLOGY1 , 1981 .

[21]  Rebecca Hincks Speech technologies for pronunciation feedback and evaluation , 2003, ReCALL.