8,041 research outputs found
The effect of automatic speech recognition EyeSpeak software on Iraqi students’ English pronunciation: a pilot study
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
TRANSDUCER FOR AUTO-CONVERT OF ARCHAIC TO PRESENT DAY ENGLISH FOR MACHINE READABLE TEXT: A SUPPORT FOR COMPUTER ASSISTED LANGUAGE LEARNING
There exist some English literary works where some archaic words are still used; they are relatively distinct from Present Day English (PDE). We might observe some archaic words that have undergone regular changing patterns: for instances, archaic modal verbs like mightst, darest, wouldst. The –st ending historically disappears, resulting on might, dare and would. (wouldst > would). However, some archaic words undergo distinct processes, resulting on unpredictable pattern; The occurrence frequency for archaic english pronouns like thee ‘you’, thy ‘your’, thyself ‘yourself’ are quite high. Students that are Non-Native speakers of English might come across many difficulties when they encounter English texts which include these kinds of archaic words. How might computer be a help for the student? This paper aims on providing some supports from the perspective of Computer Assisted Language Learning (CALL). It proposes some designs of lexicon transducers by using Local Grammar Graphs (LGG) for auto-convert of the archaic words to PDE in a literature machine readable text. The transducer is applied to a machine readable text that is taken from Sir Walter Scott’s Ivanhoe. The archaic words in the corpus can be converted automatically to PDE. The transducer also allows the presentation of the two forms (Arhaic and PDE), the PDE lexicons-only, or the original (Archaic Lexicons) form-only. This will help students in understanding English literature works better. All the linguistic resources here are machine readable, ready to use, maintainable and open for further development. The method might be adopted for lexicon tranducer for another language too
Automatic Pronunciation Assessment -- A Review
Pronunciation assessment and its application in computer-aided pronunciation
training (CAPT) have seen impressive progress in recent years. With the rapid
growth in language processing and deep learning over the past few years, there
is a need for an updated review. In this paper, we review methods employed in
pronunciation assessment for both phonemic and prosodic. We categorize the main
challenges observed in prominent research trends, and highlight existing
limitations, and available resources. This is followed by a discussion of the
remaining challenges and possible directions for future work.Comment: 9 pages, accepted to EMNLP Finding
Annotation Graphs and Servers and Multi-Modal Resources: Infrastructure for Interdisciplinary Education, Research and Development
Annotation graphs and annotation servers offer infrastructure to support the
analysis of human language resources in the form of time-series data such as
text, audio and video. This paper outlines areas of common need among empirical
linguists and computational linguists. After reviewing examples of data and
tools used or under development for each of several areas, it proposes a common
framework for future tool development, data annotation and resource sharing
based upon annotation graphs and servers.Comment: 8 pages, 6 figure
- …