Speaker Identification for Business-Card-Type Sensors

Yamaguchi, Shunpei; Oshima, Ritsuko; Oshima, Jun; Shiina, Ryota; Fujihashi, Takuya; Saruwatari, Shunsuke; Watanabe, Takashi

doi:10.1109/ojcs.2021.3075469

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…We evaluated the accuracy and validity of our proposed speaker identification. Our paper [37] shows the proposed system accurately identifies speakers under different numbers of users, environmental noises, and reverberation conditions as well as for long or short utterances. The experimental evaluations of collaborative learning show that the proposed speaker identification streamlines transcription of learners' utterance for collaborative learning analysis in each experimental case.…”

Section: Results On Speaker Identificationmentioning

confidence: 84%

“…The proposed sensor accurately acquires sound pressure, acceleration, and infrared data with synchronization across the sensors. We have shown that 1) the proposed sensors achieve synchronization accuracy less than the error of 1 ms for acquired sensor data sampled at 100 Hz [32], 2) the learning analysis algorithm extracts social graph, learning phases, and speakers [33], [34], [35], and 3) the algorithm improves the accuracy of speaker identification under various environments [36], [37]. This paper finally develops an IoT system with business card-type sensors for collaborative learning analysis.…”

Section: Collaborative Extraction Using Business Card-typementioning

confidence: 99%

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An IoT System with Business Card-Type Sensors for Collaborative Learning Analysis

Yamaguchi

Ohtawa

Oshima

et al. 2022

Journal of Information Processing

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Collaborative learning practices foster the ability to solve creative problems in collaboration with other learners. The collaboration enables learners to learn new ideas from other learners and enhances the social ability of the learners through interaction with other learners. Although the learning science field now uses qualitative analysis to analyze the effects of the collaborative discourse, qualitative analysis requires much human and time costs to analyze the collaborative discourse with dozens of students. This study proposes Sensor-based Regulation Profiler to reduce the analysis costs. The proposed scheme consists of the business card-type sensors that acquire sensor data from each learner with a precise time synchronization as well as learning analysis methods that analyze the collaborative discourse from the acquired sensor data. Experimental evaluations using the proposed scheme showed that the proposed business card-type sensors realized a time synchronization error of 7.7 μs on average across the sensors. In addition, the proposed learning analysis could extract and visualize the collaborative activity of each learner in the collaborative discourse through the social graph extraction, learning phase extraction, speaker identification, and activity estimation by using the sensor data from the proposed business card-type sensors.

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Section: Results On Speaker Identificationmentioning

confidence: 84%

Section: Collaborative Extraction Using Business Card-typementioning

confidence: 99%

An IoT System with Business Card-Type Sensors for Collaborative Learning Analysis

Yamaguchi

Ohtawa

Oshima

et al. 2022

Journal of Information Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The appropriate speech threshold depends the learning environment. The study in [3] reported that the appropriate thresholds are 75 dB and 85 dB under a no-noise environment and office environment, respectively, similar to the learning environment. Activity estimation visualizes each learner's movement as an L2norm across three-axis acceleration.…”

Section: Usagementioning

confidence: 84%

“…The user can select the parameters of start and end times (s) for analysis duration, window size and slide width (s) for sliding windows in the algorithm of speaker identification, and speech threshold (dB) for the algorithm. The study in [3] showed that the algorithm accurately identifies a speaker with window size and slide width of 2 s and 1 s, respectively. The appropriate speech threshold depends the learning environment.…”

Section: Usagementioning

confidence: 99%

Web Services for Collaboration Analysis With IoT Badges

et al. 2022

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Collaborative learning is an educational approach to teaching and learning that involves groups of learners collaborating to solve a problem, complete a task, or create a product. To enhance the performance of collaborative learning, the studies in [1]-[4] develop an IoT system and quantitatively extract collaboration between learners. The studies acquire sensor data from IoT badges on learners and analyze learning activities with the acquired sensor data on a computer. However, existing studies are not userfriendly for learning analysts who are unfamiliar with information technology owing to complex software installation and command line interface (CLI) operation. Such drawbacks hinder the wide expansion of technology and the exploration of new learning patterns in learning science. Considering high usability for analysts, this paper proposes novel web services named Sensor-based Regulation Profiler Web Services (SRP Web Services) for collaboration analysis with IoT badges. The proposed web application consists of front-end on Next.js and back-end on FastAPI, SQLite, and Python and extracts key points in learning activities for the analysts from the acquired sensor data on a web browser. Experimental evaluations showed that the proposed web services support learning analysts in quantitative analysis of learning activities with high usability. In addition, SRP Web Services are scalable with hundreds of users.

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A Web Application with Business Card-Type Sensors for Collaborative Learning Analysis

Yamaguchi

Oshima

et al. 2021

2021 IEEE 10th Global Conference on Consumer Electronics (GCCE)

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Speaker Identification for Business-Card-Type Sensors

Cited by 7 publications

References 44 publications

An IoT System with Business Card-Type Sensors for Collaborative Learning Analysis

An IoT System with Business Card-Type Sensors for Collaborative Learning Analysis

Web Services for Collaboration Analysis With IoT Badges

A Web Application with Business Card-Type Sensors for Collaborative Learning Analysis

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