Voice recognition software accuracy with second language speakers of English

Coniam, David

doi:10.1016/s0346-251x(98)00049-9

Cited by 62 publications

(42 citation statements)

References 6 publications

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“…Although word accuracy was substantially higher than clause accuracy, teachers were impressed with the manner in which the program was able to handle continuous speech and the general accuracy produced by the programresults which are generally in line with those reported through an experiment with some non-native Hong Kong speakers of English (Coniam, 1998). This was an application that teachers felt optimistic about being generally embraced into CALL programs, and this holds out a lot more promise for human-machine interaction.…”

Section: Critiquing 'Intelligent' Callsupporting

confidence: 58%

“…Previous generations of commercially available VR products (e.g., Kurzweil's Voice (1996)) required discrete speech as the form of input; that is, each word has to be uttered in citation form, with a pause between each word. The new generation of VR is that of continuous voice recognition, where pauses between words are not necessary (see Coniam (1998) for a discussion of the potential and problems associated with using current VR technology in ESL teaching).…”

Section: Voice Recognitionmentioning

confidence: 99%

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Using Language Engineering Programs to Raise Awareness of Future CALL Potential

Coniam

2004

Computer Assisted Language Learning

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This paper describes a series of awareness-raising activities for English language secondary school teachers-in-training -with a view to evaluating future, that is, 'intelligent', CALL for English language teaching. The paper first puts the development of CALL applications into perspective (after Atwell, 1999;Warschauer and Healey, 1998) in terms of its impact on ESL teachers. The paper then describes a series of activities intended to raise English language teachers' awareness and to enable them to evaluate advances in IT technology -as these will affect ESL teaching and learning. In order to achieve this future perspective, the activities described centre around language engineering software that than current CALL software per se. The paper makes reference to the secondary education system in Hong Kong, where there has been a strong push by the government for IT to support the classroom teaching of all subjects by at least 25%. For such a policy to even begin to be achieved, and for the classroom use of IT to be appropriate and effective, two conditions need to be in place. Firstly, teachers need to be familiar with and comfortable in the use of ITspecifically, the widening range of language engineering software. Secondly, they need to be able to evaluate the possibilities that IToffers. It is only if these two conditions are met that there is a greater chance that future use of CALL will be integrated into teaching in a worthwhile manner.

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Section: Critiquing 'Intelligent' Callsupporting

confidence: 58%

Section: Voice Recognitionmentioning

confidence: 99%

Using Language Engineering Programs to Raise Awareness of Future CALL Potential

Coniam

2004

Computer Assisted Language Learning

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“…Three two-hour lectures over a three-week period were delivered to the participants by a non-native English-speaking lecturer in information systems. Coniam (1999) carried out a study to explore the potential of the use of voice-recognition technology with second-language speakers of English. A small group of very competent second-language subjects participated in the study.…”

Section: Str Applications For Learningmentioning

confidence: 99%

“…According to Kheir and Way (2006), Windows® Speech Recognition using the Microsoft® Operating System is similar to other commercial and open-source products in terms of functions and performance, and is easy to use with no additional cost. Alapetite et al (2009), Coniam (1999, Jones (2005) and Wald and Bain (2008) suggested that training in STR technology should be carried out in order to achieve reasonable dictation accuracy (<85%). Therefore, the participants of this study had training in the use of the STR technology first and applied it during their presentations after achieving a good STR accuracy rate.…”

Section: Speech-to-text Recognition Technology and Trainingmentioning

confidence: 99%

Investigating applications of speech-to-text recognition technology for a face-to-face seminar to assist learning of non-native English-speaking participants

Shadiev

Hwang

Huang

et al. 2015

Technology, Pedagogy and Education

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2015):Investigating applications of speech-to-text recognition technology for a face-to-face seminar to assist learning of non-native English-speaking participants, Technology, Pedagogy and Education, This study applied speech-to-text recognition (STR) technology to assist non-native English-speaking participants to learn at a seminar given in English. How participants used transcripts generated by the STR technology for learning and their perceptions toward the STR were explored. Three main findings are presented in this study. Most participants perceived that transcripts were useful for learning. Nineteen learning strategies to use transcripts were discovered; transcripts were used to understand the topic presented, to find an answer to a question and to write summaries. Participants with different learning achievements demonstrated different learning behaviours when using transcripts. That is, some participants used transcripts effectively (i.e. studied them thoroughly and used the most important parts of them for summaries along with elaborating ideas) but some participants did not (i.e. studied transcripts superficially and employed the copy-and-paste method to complete summaries). Therefore, this study suggests how to teach participants to use transcripts by employing learning strategies meaningfully.

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“…Other studies have suggested that ASR is beneficial for pronunciation instruction (Auralog, 1995;Coniam, 1999;Neri, Cucchiarini, Strik, & Boves, 2002;Hardison, 'l Co., n.d.) or reading (Kartal, 2006); a further step could be taken to investigate its effectiveness on teaching of other aspects of oral skills. More advanced EFL learners may need more than pronunciation training; to illustrate, college students who have received at least six years of classroom instruction in English may need instruction on other aspects of oral skills such as communication strategies, suprasegmental training.…”

Section: Introductionmentioning

confidence: 99%

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

Chiu

Liou

Yeh

2007

Computer Assisted Language Learning

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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 nonEnglish 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.

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Voice recognition software accuracy with second language speakers of English

Cited by 62 publications

References 6 publications

Using Language Engineering Programs to Raise Awareness of Future CALL Potential

Using Language Engineering Programs to Raise Awareness of Future CALL Potential

Investigating applications of speech-to-text recognition technology for a face-to-face seminar to assist learning of non-native English-speaking participants

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

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