Assessment of an Index for Measuring Pronunciation Difficulty

This study assesses an index for measur-ing the pronunciation difficulty of sen-tences (henceforth, pronounceability) based on the normalized edit distance from a reference sentence to a transcrip-tion of learners’ pronunciation. Pro-nounceability should be examined when language teachers use a computer-assisted language learning system for pronunciation learning to maintain the motivation of learners. However, unlike the evaluation of learners’ pronunciation performance, previous research did not focus on pronounceability not only for English but also for Asian languages. This study found that the normalized edit distance was reliable but not valid. The lack of validity appeared to be because of an English test used for determining the proficiency of learners.

[1]  M. Hwang.  How Strategies Are Used to Solve Listening Difficulties: Listening Proficiency and Text Level Effect , 2005 .

[2]  L. Cronbach.  Essentials of psychological testing , 1960 .

[3]  Christos Koniaris.  An Approach to Measure Pronunciation Similarity in Second Language Learning Using Radial Basis Function Kernel , 2014 .

[4]  清川 英男.  A Formula for Predicting Listenability--the Listenability of English Language Materials-2- , 1990 .

[6]  Martijn Wieling,et al.  Measuring foreign accent strength in English : Validating Levenshtein distance as a measure , 2014 .

[7]  Danielle S. McNamara,et al.  Predicting Text Comprehension, Processing, and Familiarity in Adult Readers: New Approaches to Readability Formulas , 2017, Discourse Processes.

[8]  Yik-Cheung Tam,et al.  PLASER: Pronunciation Learning via Automatic Speech Recognition , 2003, HLT-NAACL 2003.

[9]  James Dean Brown,et al.  Testing in language programs , 1996 .

[10]  Wei Li,et al.  Improving non-native mispronunciation detection and enriching diagnostic feedback with DNN-based speech attribute modeling , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Keikichi Hirose,et al.  Automatic Chinese pronunciation error detection using SVM trained with structural features , 2012, 2012 IEEE Spoken Language Technology Workshop (SLT).

[12]  Feiyu Xu,et al.  A System Demonstration of a Framework for Computer Assisted Pronunciation Training , 2015, ACL.

[13]  Sze-Chu Liu,et al.  Teaching Pronunciation with Computer Assisted Pronunciation Instruction in a Technological University , 2016 .

[14]  Su-Youn Yoon,et al.  Spoken Text Difficulty Estimation Using Linguistic Features , 2016, BEA@NAACL-HLT.

[15]  Degang Lai.  A Study on the Influencing Factors of Online Learners’ Learning Motivation , 2015 .

[16]  Irving E. Fang,et al.  The “Easy listening formula” , 1966 .

[17]  E. Vajda.  Handbook of the International Phonetic Association: A Guide to the Use of the International Phonetic Alphabet , 2000 .

[18]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[19]  Steve J. Young,et al.  Phone-level pronunciation scoring and assessment for interactive language learning , 2000, Speech Commun..

[20]  Yukari Hirata,et al.  Computer Assisted Pronunciation Training for Native English Speakers Learning Japanese Pitch and Durational Contrasts , 2004 .

[21]  M. Gósy,et al.  THE DEVELOPMENT OF A HUNGARIAN–ENGLISH LEARNER SPEE CH DATABASE AND A RELATED ANALYSIS OF FILLED PAUSES , 2015 .