Synote: Accessible and Assistive Technology Enhancing Learning for All Students

Wald, Mike

doi:10.1007/978-3-642-14100-3_27

Cited by 7 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, there is the option of using auto-captioning services, with You-Tube year-on-year improving the quality of this feature. Another example is Synote (Wald, 2010) interfacing with textto-speech functionalities. In this scenario, text-to-speech software generates the captions without human intervention (Fichten et al, 2014).…”

Section: Literature On Automated Captioningmentioning

confidence: 99%

Automated generation of “good enough” transcripts as a first step to transcription of audio-recorded data

Bokhove¹,

Christopher²

2018

Preprint

View full text Add to dashboard Cite

In the last decade automated captioning services have appeared in mainstream technology use. Until now the focus of these services have been on the technical aspects, supporting pupils with special educational needs and supporting teaching and learning of second language students. Only limited explorations have been attempted regarding its use for research purposes: transcription of audio recordings. This paper presents a proof-of-concept exploration utilising three examples of automated transcription of audio recordings from different contexts; an interview, a public hearing and a classroom setting, and compares them against ‘manual’ transcription techniques in each case. It begins with an overview of literature on automated captioning and the use of voice recognition tools for the purposes of transcription. An account is provided of the specific processes and tools used for the generation of the automated captions followed by some basic processing of the captions to produce automated transcripts. Originality checking software was used to determine a percentage match between the automated transcript and a manual version as a basic measure of the potential usability of each of the automated transcripts. Some analysis of the more common and persistent mismatches observed between automated and manual transcripts is provided, revealing that the majority of mismatches would be easily identified and rectified in a review and edit of the automated transcript. Finally, some of the challenges and limitations of the approach are considered. These limitations notwithstanding, we conclude that this form of automated transcription provides ‘good enough’ transcription for first versions of transcripts. The time and cost advantages of this could be considerable, even for the production of summary or gisted transcripts.

show abstract

Section: Literature On Automated Captioningmentioning

confidence: 99%

Automated generation of “good enough” transcripts as a first step to transcription of audio-recorded data

Bokhove¹,

Christopher²

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Though audio recording of class notes is easily achieved with a student tape recorder or a classroom recording system provided through the school, acquiring the actual text transcripts of class lectures is vitally important. The addition of text transcripts versus purely audio enhances accessibility [9], [10]. Pure audio recordings can be effective for nonnative English speakers and students with learning disabilities who may simply need more time to comprehend the instructor's oral lecture.…”

Section: Notetaking For Students With Special Needsmentioning

confidence: 99%

“…Synote (www.synote.org/synote), a web-based application, created synchronized bookmarks or "Synmarks" that contain notes and tags to synchronize audio or video recordings, SR transcripts and PowerPoint slide images [10]. These individual files are compiled into a synchronized multimedia web resource for easy access, searching, and management by students and teachers.…”

Section: Display Of Multimedia Lecture Notesmentioning

confidence: 99%

Using speech recognition for real-time captioning and lecture transcription in the classroom

Ranchal

Taber‐Doughty

Guo

et al. 2013

IEEE Trans. Learning Technol.

View full text Add to dashboard Cite

Speech recognition (SR) technologies were evaluated in different classroom environments to assist students to automatically convert oral lectures into text. Two distinct methods of SR-mediated lecture acquisition (SR-mLA), real-time captioning (RTC) and postlecture transcription (PLT), were evaluated in situ life and social sciences lecture courses employing typical classroom equipment. Both methods were compared according to technical feasibility and reliability of classroom implementation, instructors' experiences, word recognition accuracy, and student class performance. RTC provided near-instantaneous display of the instructor's speech for students during class. PLT employed a user-independent SR algorithm to optimally generate multimedia class notes with synchronized lecture transcripts, instructor audio, and class PowerPoint slides for students to access online after class. PLT resulted in greater word recognition accuracy than RTC. During a science course, students were more likely to take optional online quizzes and received higher quiz scores with PLT than when multimedia class notes were unavailable. Overall class grades were also higher when multimedia class notes were available. The potential benefits of SR-mLA for students who have difficulty taking notes accurately and independently were discussed, particularly for nonnative English speakers and students with disabilities. Field-tested best practices for optimizing SR accuracy for both SR-mLA methods were outlined.

show abstract

“…Finally, there is the option of using auto-captioning services, with YouTube year-on-year improving the quality of this feature. Another example is Synote (Wald, 2010) interfacing with text-to-speech functionalities. In this scenario, text-to-speech software generates the captions without human intervention (Fichten et al, 2014).…”

Section: Relevant Literaturementioning

confidence: 99%

Automated generation of ‘good enough’ transcripts as a first step to transcription of audio-recorded data

Bokhove

Downey

2018

Methodological Innovations

View full text Add to dashboard Cite

In the last decade, automated captioning services have appeared in mainstream technology use. Until now, the focus of these services have been on the technical aspects, supporting pupils with special educational needs and supporting teaching and learning of second language students. Only limited explorations have been attempted regarding its use for research purposes: transcription of audio recordings. This article presents a proof-of-concept exploration utilising three examples of automated transcription of audio recordings from different contexts; an interview, a public hearing and a classroom setting, and compares them against 'manual' transcription techniques in each case. It begins with an overview of literature on automated captioning and the use of voice recognition tools for the purposes of transcription. An account is provided of the specific processes and tools used for the generation of the automated captions followed by some basic processing of the captions to produce automated transcripts. Originality checking software was used to determine a percentage match between the automated transcript and a manual version as a basic measure of the potential usability of each of the automated transcripts. Some analysis of the more common and persistent mismatches observed between automated and manual transcripts is provided, revealing that the majority of mismatches would be easily identified and rectified in a review and edit of the automated transcript. Finally, some of the challenges and limitations of the approach are considered. These limitations notwithstanding, we conclude that this form of automated transcription provides 'good enough' transcription for first versions of transcripts. The time and cost advantages of this could be considerable, even for the production of summary or gisted transcripts.

show abstract

Synote: Accessible and Assistive Technology Enhancing Learning for All Students

Cited by 7 publications

References 13 publications

Automated generation of “good enough” transcripts as a first step to transcription of audio-recorded data

Automated generation of “good enough” transcripts as a first step to transcription of audio-recorded data

Using speech recognition for real-time captioning and lecture transcription in the classroom

Automated generation of ‘good enough’ transcripts as a first step to transcription of audio-recorded data

Contact Info

Product

Resources

About