Using Automatic Speech Recognition to Facilitate English Pronunciation Assessment and Learning in an EFL Context

2021 ◽  
Vol 11 (3) ◽  
pp. 74-91
Author(s):  
Wenqi Xiao ◽  
Moonyoung Park
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Assessment Tool ◽  
Read Aloud ◽  
Learning Needs ◽  
Learning Tool ◽  
Efl Learners ◽  
Efl Context ◽  
English Pronunciation ◽  
Error Types

With the advancement of automatic speech recognition (ASR) technology, ASR-based pronunciation assessment can diagnose learners' pronunciation problems. Meanwhile, ASR-based pronunciation training allows more opportunities for pronunciation practice. This study aims to investigate the effectiveness of ASR technology in diagnosing English pronunciation errors and to explore teachers' and learners' attitudes towards using ASR technology as a pronunciation assessment tool and as a learning tool. Five Chinese EFL learners participated in read-aloud tests, including a human-assessed test and an ASR-assessed test. Pronunciation error types diagnosed by the two tests were compared to determine the extent of overlapping areas. The findings demonstrate that there were overlaps between human rating and machine rating at the segmental level. Moreover, it was found that learners' varied pronunciation learning needs were met by using the ASR technology. Implications of the study will provide insights relevant to using ASR technology to facilitate English pronunciation assessment and learning.

The Effects of Communicative Pronunciation Instruction on Suprasegmental Performance in an EFL Context

2017 ◽  
Vol 7 (10) ◽  
pp. 860
Author(s):  
Nasrin Shah Mohammad Nazari ◽  
Atefeh Sadat Mirsaeeidi
Keyword(s):  
Language Production ◽  
Explicit Instruction ◽  
Read Aloud ◽  
Control Group ◽  
Efl Learners ◽  
Efl Context ◽  
Pronunciation Instruction ◽  
Picture Description ◽  
Based Instruction ◽  
Learned Features

This study investigated the effects of communicative suprasegmental instruction on Iranian EFL learners’ pronunciation performance. To this end, 24 pre-intermediate EFL learners were randomly assigned to two groups: the experimental group receiving communicative pronunciation instruction in which after receiving conventional explicit instruction students were given communicative tasks to practice learned features, and the control group receiving only conventional explicit exercise-based instruction. The learners’ pronunciations were assessed in controlled read-aloud and communicative picture-description/picture-driven contexts in terms of two suprasegmental features (i.e. compound words stress and interrogative intonation). The results of the study revealed that the explicit exercise-based instruction was significantly effective in controlled contexts but modestly effective in communicative picture-description and picture-driven tasks. On the contrary, communicative pronunciation instruction was not only significantly effective in the controlled context but also in communicative tasks. This finding reveals that communicative suprasegmental instruction is more effective than conventional explicit instruction in both controlled and communicative language production contexts. In the end, some pedagogical implications of the findings are also discussed.

scholarly journals The Usefulness of Automatic Speech Recognition (ASR) Eyespeak Software in Improving Iraqi EFL Students’ Pronunciation

2017 ◽  
Vol 8 (1) ◽  
pp. 221 ◽  
Author(s):  
Lina Fathi Sidig Sidgi ◽  
Ahmad Jelani Shaari
Keyword(s):  
College Students ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Native Language ◽  
First Year ◽  
First Year College ◽  
Pronunciation Instruction ◽  
English Pronunciation ◽  
Efl Students ◽  
First Year College Students

The present study focuses on determining whether automatic speech recognition (ASR) technology is reliable for improving English pronunciation to Iraqi EFL students. Non-native learners of English are generally concerned about improving their pronunciation skills, and Iraqi students face difficulties in pronouncing English sounds that are not found in their native language (Arabic). This study is concerned with ASR and its effectiveness in overcoming this difficulty. The data were obtained from twenty participants randomly selected from first-year college students at Al-Turath University College from the Department of English in Baghdad-Iraq. The students had participated in a two month pronunciation instruction course using ASR Eyespeak software. At the end of the pronunciation instruction course using ASR Eyespeak software, the students completed a questionnaire to get their opinions about the usefulness of the ASR Eyespeak in improving their pronunciation. The findings of the study revealed that the students found ASR Eyespeak software very useful in improving their pronunciation and helping them realise their pronunciation mistakes. They also reported that learning pronunciation with ASR Eyespeak enjoyable.  

Developing an Alternative Math Assessment Tool Using Speech Recognition

2014 ◽  
Vol 1 (1) ◽  
pp. 90-96
Author(s):  
Love Jhoye Raboy ◽  
April Mae Ablon ◽  
Irish Jane Cabulay ◽  
Recy Mae Mendoza ◽  
Guilda Marie Nanaman
Keyword(s):  
Speech Recognition ◽  
Assessment Tool ◽  
System Development ◽  
Data Gathering ◽  
Read Aloud ◽  
Graphical Display ◽  
Speech Technology ◽  
Math Assessment ◽  
Interdisciplinary Research Project ◽  
Reading Tutor

The world today is now in the era of Information Technology. The development of ICT-based processes specifically in the area of assessment in school is now visible. Project LISTEN (Literacy Innovation that Speech Technology ENables) is an interdisciplinary research project at Carnegie Mellon University to develop a novel tool to improve literacy – an automated Reading Tutor that displays stories on a computer screen, and listens to children read aloud. This study does not provide right or wrong answers for they let the user evaluate the answer. The main objective of this study is to develop an Alternative Math Assessment Tool for Preschoolers using Speech Recognition. This software aims to assist teachers in the review of Math lessons for preschoolers using speech recognition. The development of the system utilizes the System Development Cycle approach that includes data gathering to identify system’s expected functionalities, designing the system using Use-Case Diagram, integration of JSAPI for Voice Recognition, using Synthesizer software for reading the questions out loud, a graphical display of teacher representation and a graphical display for every question in the review. Along in the development of this assessment tool is the implementation of the system. The system was developed using the Java Programming language. It also uses a MySql database to store data for preschoolers, review questions and text answers. In the conduct of the review digital microphone and a speaker is needed. The developed system is capable of creating questions for a particular review, activating a review for the preschooler to take and record the preschooler’s scores at every end of the review. The system also includes a graphical display of questions. In the conduct of the review, the system was able to read out loud the questions, and a 5-second time span for the pupil to answer the review questions. The system will listen and the feedback from the study will display the correctly uttered answer. User testing results indicate an 83% correct response of the system against the correct uttered answer of the preschooler.

Improving English pronunciation via automatic speech recognition technology

2019 ◽  
Vol 25 (2) ◽  
pp. 126
Author(s):  
Minjia Liu ◽  
Xiujuan Chen ◽  
Yiling Mo ◽  
Zejia Chen ◽  
Xiaobin Liu ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Speech Recognition Technology ◽  
English Pronunciation

scholarly journals Automatic speech recognition in computer-assisted language learning for individual learning in speaking

2020 ◽  
Vol 5 (2) ◽  
pp. 193-197
Author(s):  
Esti Junining ◽  
Sony Alif ◽  
Nuria Setiarini
Keyword(s):  
Speech Recognition ◽  
Foreign Language ◽  
Language Learning ◽  
Programming Language ◽  
Automatic Speech Recognition ◽  
Computer Assisted ◽  
Computer Assisted Language Learning ◽  
Efl Learners ◽  
C Programming Language ◽  
C Programming

This study is intended to help English as a Foreign Language (EFL) learners in Indonesia to reduce their anxiety level while speaking in front of other people. This study helps to develop an atmosphere that encourages students to practice speaking independently. The interesting atmosphere can be obtained by using Automatic Speech Recognition (ASR) where every student can practice speaking individually without feeling anxious or pressurized, because he/she can practice independently in front of a computer or a gadget. This study used research and development design as it tried to develop a product which can create an atmosphere that encourages students to practice their speaking. The instrument used is a questionnaire which is used to analyze the students’ need of learning English. This study developed a product which utilized ASR technology using C# programming language. This study revealed that the product developed using ASR can make students practice speaking individually without feeling anxious and pressurized.

scholarly journals The Effect of Automatic Speech Recognition EyeSpeak Software on Iraqi Students’ English Pronunciation: A Pilot Study

2017 ◽  
Vol 8 (2) ◽  
pp. 48
Author(s):  
Lina Fathi Sidig Sidgi ◽  
Ahmad Jelani Shaari
Keyword(s):  
Speech Recognition ◽  
Foreign Language ◽  
Language Learning ◽  
Automatic Speech Recognition ◽  
Teaching And Learning ◽  
Mother Tongue ◽  
Target Language ◽  
Computer Assisted ◽  
English Pronunciation ◽  
Sound Transformation

The use of technology, such as computer-assisted language learning (CALL), is used in teaching and learning in the foreign language classrooms where it is most needed. One promising emerging technology that supports language learning is automatic speech recognition (ASR). Integrating such technology, especially in the instruction of pronunciation in the classroom, is important in helping students to achieve correct pronunciation. In Iraq, English is a foreign language, and it is not surprising that learners commit many pronunciation mistakes. One factor contributing to these mistakes is the difference between the Arabic and English phonetic systems. Thus, the sound transformation from the mother tongue (Arabic) to the target language (English) is one barrier for Arab learners. The purpose of this study is to investigate the effectiveness of using automatic speech recognition ASR EyeSpeak software in improving the pronunciation of Iraqi learners of English. An experimental research project with a pretest-posttest design is conducted over a one-month period in the Department of English at Al-Turath University College in Baghdad, Iraq. The ten participants are randomly selected first-year college students enrolled in a pronunciation class that uses traditional teaching methods and ASR EyeSpeak software. The findings show that using EyeSpeak software leads to a significant improvement in the students’ English pronunciation, evident from the test scores they achieve after using EyeSpeak software. 

Improving English pronunciation via automatic speech recognition technology

2019 ◽  
Vol 25 (2) ◽  
pp. 126 ◽  
Author(s):  
Xiaobin Liu ◽  
Manfei Xu ◽  
Meihui Li ◽  
Meiting Han ◽  
Zejia Chen ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Speech Recognition Technology ◽  
English Pronunciation

Error Types in Natural Language Processing in Inflectional Languages

2021 ◽  
pp. 73-86
Author(s):  
Gregor Donaj ◽  
Mirjam Sepesy Maučec
Keyword(s):  
Natural Language Processing ◽  
Speech Recognition ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Automatic Speech Recognition ◽  
Slovene Language ◽  
Error Classification ◽  
Error Types

This article presents the challenges of natural language processing applications when they are used with inflectional languages. Two typical applications are presented: automatic speech recognition and machine translation. An overview of those applications and the properties of inflectional languages is given as well as examples from the highly inflectional Slovene language. Then, an error classification with examples is given, also with an emphasis on inflectional languages, as well as some directions for further research in this area.

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