Speaking: A Critical Skill and a Challenge
ibal kula
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Abstract
Speaking is at the heart of second language learning but has been somewhat ignored in teaching and testing for a number of logistical reasons. Automatic Speech Recognition (ASR) can give speaking a central role in language instruction. This article describes plans and efforts to shape speech-interactive Computer-Assisted Language Learning (CALL) programs. Current proficiency guidelines provide a practical framework for this development. Although questions and challenges remain, current implementations of ASR provide some solutions now, and ongoing research holds great promise for future implementations.
Related papers
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Proc. ICPhS, 2003
In this paper, we examine various studies and reviews on the usability of Automatic Speech Recognition (ASR) technology as a tool to train pronunciation in the second language (L2). We show that part of the criticism that has been addressed to this technology is not warranted, being rather the result of limited familiarity with ASR technology and with broader Computer Assisted Language Learning (CALL) courseware design matters. In our analysis we also consider actual problems of state-of-the-art ASR technology, with a view to indicating how ASR can be employed to develop courseware that is both pedagogically sound and reliable.
ASR-based CALL systems and learner speech data: new resources and opportunities for research and development in second language learning
2014
In this paper we describe the language resources developed within the project Feedback and the Acquisition of Syntax in Oral Proficiency (FASOP), which is aimed at investigating the effectiveness of various forms of practice and feedback on the acquisition of syntax in second language (L2) oral proficiency, as well as their interplay with learner characteristics such as education level, learner motivation and confidence. For this purpose, use is made of a Computer Assisted Language Learning (CALL) system that employs Automatic Speech Recognition (ASR) technology to allow spoken interaction and to create an experimental environment that guarantees as much control over the language learning setting as possible. The focus of the present paper is on the resources that are being produced in FASOP. In line with the theme of this conference, we present the different types of resources developed within this project and the way in which these could be used to pursue innovative research in ...
SPEECH TECHNOLOGY IN COMPUTER-AIDED LANGUAGE LEARNING: STRENGTHS AND LIMITATIONS OF A NEW CALL PARADIGM
1998
We investigate the suitability of deploying speech technology in computer-based systems that can be used to teach foreign language skills. In reviewing the current state of speech recognition and speech processing technology and by examining a number of voice-interactive CALL applications, we suggest how to create robust interactive learning environments that exploit the strengths of speech technology while working around its limitations. In the conclusion, we draw on our review of these applications to identify directions of future research that might improve both the design and the overall performance of voice-interactive CALL systems.
Speech technology for language tutoring
Proceedings of LangTech, 2008
Language learners are known to perform best in one-on-one interactive situations in which they receive optimal corrective feedback. However, one-on-one tutoring by trained language instructors is costly and therefore not feasible for the majority of language learners. This particularly applies to oral proficiency, which requires intensive tutoring. Computer Assisted Language Learning (CALL) systems that make use of Automatic Speech Recognition (ASR) seem to offer new perspectives for language tutoring. In this paper we explain how.
DISCO: development and integration of speech technology into courseware for language learning
Recent research has shown that a properly designed ASR- based CALL system (Dutch-CAPT) was capable of detecting pronunciation errors and of providing comprehensible feedback on pronunciation. Since pronunciation is not the only skill required for speaking a second language, we explored the possibility of extending the Dutch-CAPT approach to other aspects of speaking proficiency like morphology and syntax. In this paper we explain how a number of errors in morphology and syntax that are common in spoken Dutch L2 could be addressed in an ASR-based CALL system. Finally, we present our new project in which corrective feedback will be provided on all three aspects of spoken proficiency: pronunciation, morphology and syntax. Index Terms: pronunciation training, CALL, ASR, error detection.
Automatic Speech Recognition to assist
2015
The potential use of Automatic Speech Recognition to assist receptive communication is explored. The opportunities and challenges that this technology presents students and staff to provide captioning of speech online or in classrooms for deaf or hard of hearing students and assist blind, visually impaired or dyslexic learners to read and search learning material more readily by augmenting synthetic speech with natural recorded real speech is also discussed and evaluated. The automatic provision of online lecture notes, synchronised with speech, enables staff and students to focus on learning and teaching issues, while also benefiting learners unable to attend the lecture or who find it difficult or impossible to take notes at the same time as listening, watching and thinking.
Computer Assisted Language Learning (CALL)
A Handbook for English Language Laboratories, 2009
The use of the English language continues to be viable and veritable across the countries of the continent, even in the non-native environments such as Nigeria, Ghana, and India, where English is the official language. As an index of achievement within these non-native contexts, there is a great premium on learning the various aspects of English. The psycholinguistic circumstances of teaching and learning English in such contexts call for concerted pedagogical efforts to satisfy the expected learning outcomes. Thus, within the field of Computer-Assisted Language Learning (CALL), an objective of this paper is to identify the various technology that can assist the teaching, learning and improvement of the English pronunciation. The second objective is to illustrate with the PRAAT software the applicability of technology for the improvement of the English pronunciation. Structurally, the paper is divided into two parts: Part One identifies some technology generally applicable to language teaching, learning and improvement. It lists 35 types of technology that possess the propensity to improve the English pronunciation. This list is divided into four different categories, namely: Hardware (13: 1-13), Software (9, 14-22), Hardsoftware (5,[23][24][25][26][27] and Virtual Technology (9,[28][29][30][31][32][33][34][35]. Some of these are summarised into four Plates (Plates 1-4). Part Two of the paper discusses PRAAT as one of the best and most current software for learning, teaching and improving oneself in the English pronunciation. It illustrates with 11 screen captures (Pictures 1-11) the viability and applicability of the software to language learning, using This is Babcock University, Ilishan. The paper recommends a simulation of the native-like environments offered by the Computer-Assisted Language Learning technology for the desired proficiency of language learning, at this instance, the English pronunciation.
Elsa Speak App: Automatic Speech Recognition (ASR) for Supplementing English Pronunciation Skills
Pedagogy: Journal of English Language Teaching, 2021
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Evaluating a Speech Training Software Program Called NativeAccent Based on Empirical Studies
Journal of English Learner Education, 2020
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A CALL system for practicing speaking proficiency : pronunciation, morphology and syntax
Recent research has shown that a properly designed ASR-based CALL system is capable of detecting pronunciation errors and of providing comprehensible corrective feedback on pronunciation. In the DISCO project we extend this approach to other aspects of speaking proficiency like morphology and syntax. For detecting syntactic errors it is sufficient to know which words were spoken in which order. Morphological and pronunciation errors require a more detailed analysis at the segmental level. Specific speech technology algorithms are developed for detecting errors on pronunciation, morphology, and syntax. These algorithms will be tested off-line with non-native data, and online with language learners.
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Computer Assitsed Language Learning Speaking Skills
Article, 2023
The digital period has aided the advancement of English language education. Web users today have more exposure to English language learning than ever before. CALL (Computer Assisted Language Learning) is still being touted as a great option for language learning, although it is still in its early phases of development. A bold new fascinating world is certainly indicated by CALL, with its own set of conundrums and repercussions (Kern, 2014). The aims of the current paper were to investigate the effectiveness of CALL in improving speaking skills among language learners, explore the potential of CALL in providing personalized instruction and feedback to language learners, examine the role of technology-mediated communication in enhancing language learners' oral proficiency and develop and evaluate new CALL tools and materials for improving language learners' speaking skills. The key objectives of the current research paper were to assess the impact of CALL on language learners' speaking skills development, including fluency, accuracy, complexity, and comprehensibility, and to investigate the effectiveness of different types of CALL activities.
Evaluating automatic speech recognition-based language learning systems: a case study
Computer Assisted Language Learning, 2016
Evaluating automatic speech recognition-based language learning systems: a case study The purpose of this research was to evaluate a prototype of an automatic speech recognition (ASR)-based language learning system that provides feedback on different aspects of speaking performance (pronunciation, morphology and syntax) to students of Dutch as a second language. We carried out usability reviews, expert reviews and user tests to gain insight into the potential of this prototype and the possible ways in which it could be further adapted or improved, with a view to developing specific language learning products. The evaluation revealed that domain experts and users (teachers and students) are generally positive about the system and intend to use it if they get the opportunity. In addition, recommendations have been made which range from specific changes and additions to the system to more general statements about the pedagogical and technological issues involved. These recommendations can be useful to improve this prototype and to develop other ASR-based systems, which can be deployed either as language courseware or as research tools to investigate design hypotheses and language acquisition processes.
Computer Assisted Language Learning and Speaking Proficiency of Second Language Learners
Asia Pacific Journal of Social and Behavioral Sciences
This study aimed to determine the speaking proficiency of L2 learners undergoing a computer-assisted language learning program in Southern Christian College in the school year 2015-2016. It specifically aimed to find the weighted study score and speech recognition score of the students in the Job Enabling English Proficiency or DynEd class, to identify their speaking proficiency, and to determine whether their scores in JEEP Start are significantly related to their speaking proficiency. Fifty students were identified to participate in the lottery method. Their scores in the JEEP Start were summarized through descriptive statistical tools like percentage, frequency count and mean, and their speaking proficiency was classified and encoded in a spreadsheet. The relationship between their scores and speaking proficiency was determined through regression analysis. Results revealed that the majority of the students had excellent WSS, and more than half of them had high speech recognition ...
Development and Integration of Speech Technology into COurseware for Language Learning: The DISCO Project
Theory and Applications of Natural Language Processing, 2012
Applying speech and language technology to foreign language education
2009 International Multiconference on Computer Science and Information Technology, 2009
In recent years modern techniques involving speech processing have been gaining increasing interest among researchers and companies involved in the integration of new technologies into second language (L2) tutoring systems. At the same time, pronunciation and prosody have finally gained due attention among L2 teachers and learners. The paper describes technical and linguistic specifications for the EURONOUNCE project whose aim is to create software which will integrate non-native speech analysis and recognition with a primary goal of detecting L2 learners' pronunciation and prosodic errors and offering multimodal feedback. The software is aimed at specific language pairs, namely Polish, Russian, Czech and Slovak learners of German and vice versa. Beside information concerning the collection, structure and annotation of the multilingual speech corpora the article outlines the feedback system as well as the Pitch Line program which can be implemented in the prosody training module of the Euronounce tutoring system.
Speech recognition for online language learning: Connecting CALL-SLT and DALIA
In this paper, we show how speech recognition can contribute to the development of a multimedia course for foreign language self-learning that focuses both on receptive and productive skills. Specifically, we describe how we merged two different existing e-leaning projects, DALIA [5] and CALL-SLT ). DALIA is a project for self-learning and blended learning which targets several European languages and proficiency levels. CALL-SLT is a web-based spoken translation game configured to teach productive competences in specific domains. This paper describes the methodology used to merge these two approaches.
Developing a CALL System for Practicing Oral Proficiency: How to Design for Speech Technology, Pedagogy and Learners
Proceedings of the SLaTE-2009 workshop, 2009
Automatic recognition of non-native speech is problematic. A key challenge in developing spoken CALL systems is to design exercises that enable learning but which are still technically feasible. This especially applies to systems intended for practicing grammar. In the current paper we focus on the issue of matching design and speech technology. On the one hand we are developing and testing speech technology modules to determine what is feasible. On the other we use this knowledge in designing a CALL system for practicing pronunciation and grammar.
Automatic Speech Recognition for CALL: A Task-Specific Application for Training Nurses
Canadian Modern Language Review/ La Revue canadienne des langues vivantes, 2011
In recent years, language researchers and teachers have attempted to put meaningful communication at the centre of learners' classroom interactions. Yet the majority of existing computer-assisted language learning (CALL) applications have relied on largely non-communicative learner-computer interactions. The challenge facing CALL developers, therefore, is to explore new ways of providing learners with communicative practice. This article reviews existing uses of automatic speech recognition in second and foreign language teaching and describes the development of an innovative interactive automatic speech recognition system for developing second language speaking skills. This system uses video clips and the EduSpeak speech recognition system to simulate a nurse-patient interview. The system allows learners (for example, health care professionals whose first language is not English) to ask questions to an English-speaking patient and to receive both meaningful responses from the p...
Application of the Speech Recognition Technology in Language Education
As technology advances and innovative communication products emerge, the interaction between people, globally, changes. Therefore, technology plays a major role in influencing both language and culture. The purpose of this paper is to answer the question of how the speech recognition technology available on the smartphones and tablets can support language learning. The very latest and advanced features of the smartphones such as the speech recognition capabilities were utilised in experimenting with language learning. The languages included French, German, Italian, Japanese and Mandarin. In all cases, the established learning concepts such as learning by guidance were considered. This paper has demonstrated how the latest technologies such as speech recognition can be utilised to produce effective educational materials for immersive language education. It is concluded that Technology can enable a learner to simulate the learning by guidance approach in the absence of the tutor or the opportunity of being in the actual environment.
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