An Illustrated Taxonomy of Online CAPT Resources

Henrichsen, Lynn

doi:10.1177/0033688220954560

Cited by 8 publications

(7 citation statements)

References 8 publications

(6 reference statements)

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“…It is worth mentioning that the ASR and TTS as well as their pedagogical implications can be applied for different learning contexts and different languages. As Henrichsen (2021) indicates, ASR technology can convert speech into text in over one hundred languages. Moreover, the aforesaid ways to use the tools can be applied to different target languages if supported by the applications.…”

Section: Integrating Asr and Tts To Pronunciation Teachingmentioning

confidence: 99%

Automatic speech recognition and text-to-speech technologies for L2 pronunciation improvement

2022

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This paper presents a reflection on two technologies – automatic speech recognition (ASR) and Text-to-Speech (TTS) – to improve learners’ pronunciation, aiming for successful spoken communication. It sheds some light on the practical usage of these technologies, demonstrating their effectiveness, qualities, and limitations to assist teachers in deciding the most efficient digital resources applied to their students’ needs. A review of literature on previous empirical studies was carried out, with quantitative and/or qualitative studies conducted by researchers in the field, investigating teachers’ and learners' perceptions and the use of ASR and TTS as a pedagogical tool for pronunciation practice. As a result, it was concluded that a) the presented resources seem to have the potential to enhance pronunciation practice, both in terms of perception and production; b) technology can result in considerable benefits to learners, mainly as a supplement to pronunciation teaching; and c) the use of these digital resources is a way of giving learners the opportunity to focus on their specific difficulties and receive personalized feedback while becoming more autonomous in their learning process.

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Section: Integrating Asr and Tts To Pronunciation Teachingmentioning

confidence: 99%

Automatic speech recognition and text-to-speech technologies for L2 pronunciation improvement

2022

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“…This vast number of ASR programs allows the language learner to speak the foreign language and see it converted instantaneously into written words. The current state of ASR technology can make this conversion much more accurate if compared to a decade ago and some ASR programs can transcribe texts into over one hundred languages (HENRICHSEN, 2020).…”

Section: Asr Technology and L2 Learningmentioning

confidence: 99%

How Well Can Asr Technology Understand Foreign-Accented Speech?

Souza

Gottardi

2022

Trab. linguist. apl.

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Following the Covid-19 pandemic, digital technology is more present in classrooms than ever. Automatic Speech Recognition (ASR) offers interesting possibilities for language learners to produce more output in a foreign language (FL). ASR is especially suited for autonomous pronunciation learning when used as a dictation tool that transcribes the learner’s speech (McCROCKLIN, 2016). However, ASR tools are trained with monolingual native speakers in mind, not reflecting the global reality of English speakers. Consequently, the present study examined how well two ASR-based dictation tools understand foreign-accented speech, and which FL speech features cause intelligibility breakdowns. English speech samples of 15 Brazilian Portuguese and 15 Spanish speakers were obtained from an online database (WEINBERGER, 2015) and submitted to two ASR dictation tools: Microsoft Word and VoiceNotebook. The resulting transcriptions were manually inspected, coded and categorized. The results show that overall intelligibility was high for both tools. However, many features of normal FL speech, such as vowel and consonant substitution, caused the ASR dictation tools to misinterpret the message leading to communication breakdowns. The results are discussed from a pedagogical viewpoint.

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“…Unlike Computer-Assisted Language Learning (CALL), which was split into CALL proper, MALL (Mobile-Assisted Language Learning; Yaman & Ekmekçi, 2016) and RALL (Robot-Assisted Language Learning; Han 2012) in response to rapid technological changes, CAPT encompasses all commonly used digital tools for pronunciation instruction. Therefore, modern CAPT encompasses a wide variety of tools which can serve a number of pedagogical goals (Henrichsen 2021).…”

Section: Computer-assisted Pronunciation Trainingmentioning

confidence: 99%

“…CAPT affordances requested by teachers and researchers to a large degree reflect educators' hopes for effective technology-assisted language instruction in general. These aspects include self-paced practice, individualized instruction, individualized feedback, authentic materials and elements of gamification (Levis 2007;Henrichsen 2021). In addition to these general requests, there exist domainspecific affordances, namely shifting some attention to suprasegmentals, the ability to accommodate acceptable variation in one's pronunciation, and learner exposure to various pronunciation models.…”

Section: Computer-assisted Pronunciation Trainingmentioning

confidence: 99%

Microsoft Reading Progress as Capt Tool

Molenda

Grabarczyk

2022

RiL

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The paper explores the accuracy of feedback provided to non-native learners of English by a pronunciation module included in Microsoft Reading Progress. We compared pronunciation assessment offered by Reading Progress against two university pronunciation teachers. Recordings from students of English who aim for native-like pronunciation were assessed independently by Reading Progress and the human raters. The output was standardized as negative binary feedback assigned to orthographic words, which matches the Microsoft format. Our results indicate that Reading Progress is not yet ready to be used as a CAPT tool. Inter-rater reliability analysis showed a moderate level of agreement for all raters and a good level of agreement upon eliminating feedback from Reading Progress. Meanwhile, the qualitative analysis revealed certain problems, notably false positives, i.e., words pronounced within the boundaries of academic pronunciation standards, but still marked as incorrect by the digital rater. We recommend that EFL teachers and researchers approach the current version of Reading Progress with caution, especially as regards automated feedback. However, its design may still be useful for manual feedback. Given Microsoft declarations that Reading Progress would be developed to include more accents, it has the potential to evolve into a fully-functional CAPT tool for EFL pedagogy and research.

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An Illustrated Taxonomy of Online CAPT Resources

Cited by 8 publications

References 8 publications

Automatic speech recognition and text-to-speech technologies for L2 pronunciation improvement

Automatic speech recognition and text-to-speech technologies for L2 pronunciation improvement

How Well Can Asr Technology Understand Foreign-Accented Speech?

Microsoft Reading Progress as Capt Tool

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