A Model for Incorporating an Automatic Speech Recognition System in a Noisy Educational Environment

Blunt, Phillip; Haskins, Bertram

doi:10.1109/imitec45504.2019.9015907

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…This section reintroduces the proposed model, which has been updated since its inception, initially published in [1]. This model is theorised through background literature review and exploration; then reinforced by a proof of concept prototype implementation and the experimentation performed to evaluate it.…”

Section: Model Overviewmentioning

confidence: 99%

“…These challenges are also further compounded by a lack of context whenever students attempt to summarise a lesson without integrating with the theoretical focus of the classroom teaching session. Toward providing a solution to these challenges of lesson summary generation; this work is an extension of the paper presented at The 2019 International Multidisciplinary Information Technology and Engineering Conference (IMITEC) wherein a model was proposed for automated lesson summary generation in a noisy educational environment using keywords in the educator's speech as the prompting mechanism for summary of key points [1]. This extension reintroduces the proposed model, along with the theoretical background it is based in, reinforced by a proof of concept prototype system for lesson summary generation which is used to demonstrate the model's application.…”

Section: Introductionmentioning

confidence: 99%

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A Model for the Application of Automatic Speech Recognition for Generating Lesson Summaries

Blunt¹,

Haskins²

2021

Adv. sci. technol. eng. syst. j.

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Automatic Speech Recognition (ASR) technology has the potential to improve the learning experience of students in the classroom. This article addresses some of the key theoretical areas identified in the pursuit of implementing a speech recognition system, capable of lesson summary generation in the educational setting. The article discusses: some of the applications of ASR technology in education; prominent feature extraction and speech enhancement techniques typically applied to digital speech; and established neural network-based machine learning models capable of keyword spotting or continuous speech recognition. Following the theoretical investigation, a model is proposed for the implementation of an automatic speech recognition system in a noisy educational environment to facilitate automated, speech-driven lesson summary generation. A prototype system was developed and improved based on this model, ultimately proving itself capable of generating a lesson summary intended to bolster students' secondary contact with lesson content. This topic-oriented lesson summary provides students with a lesson transcript, but also helps them to monitor educator-defined keyword terms, their prevalence and order as communicated in the lesson, and their associations with educatordefined sections of course content. The prototype was developed using the Python programming language with a modular approach so that its implemented Continuous Speech Recognition system and noise management technique could be chosen at run-time. The prototype contrasts the performance of CMUSphinx and Google Speech Recognition for ASR, both accessed via a cloud-based programming library, and compared the change in accuracy when applying noise injection, noise cancellation or noise reduction to the educator's speech. Proof of concept was established using the Google Speech Recognition System, which prevailed over CMUSphinx and enabled the prototype to achieve 100,00% accuracy in keyword identification and association on noise-free speech, contrasted with a 96,93% accuracy in keyword identification and association on noise-polluted speech using a noise-cancellation technique.

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Section: Model Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%