Training Based Noise Removal Technique for a Speech-to-Text Representation Model

Rashmi, S; Hanumanthappa, M.; Gopala, B

doi:10.1088/1742-6596/1142/1/012019

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…An interesting approach can be found in the work of Rashmi et al [13], where the focus is on removing noise from speech signals for Speech-to-Text conversion. Utilizing PRAAT, a phonetic tool, the study introduces a training-based noise-removal technique (TBNRT).…”

Section: Related Workmentioning

confidence: 99%

Neural Network-Based Approach to Detect and Filter Misleading Audio Segments in Classroom Automatic Transcription

Hewstone,

Araya

2023

Applied Sciences

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Audio recording in classrooms is a common practice in educational research, with applications ranging from detecting classroom activities to analyzing student behavior. Previous research has employed neural networks for classroom activity detection and speaker role identification. However, these recordings are often affected by background noise that can hinder further analysis, and the literature has only sought to identify noise with general filters and not specifically designed for classrooms. Although the use of high-end microphones and environmental monitoring can mitigate this problem, these solutions can be costly and potentially disruptive to the natural classroom environment. In this context, we propose the development of a novel neural network model that specifically detects and filters out problematic audio sections in classroom recordings. This model is particularly effective in reducing transcription errors, achieving up to a 96% success rate in filtering out segments that could lead to incorrect automated transcriptions. The novelty of our work lies in its targeted approach for low-budget, aurally complex environments like classrooms, where multiple speakers are present. By allowing the use of lower-quality recordings without compromising analysis capability, our model facilitates data collection in natural educational settings and reduces the dependency on expensive recording equipment. This advancement not only demonstrates the practical application of specialized neural network filters in challenging acoustic environments but also opens new avenues for enhancing audio analysis in educational research and beyond.

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Section: Related Workmentioning

confidence: 99%

Neural Network-Based Approach to Detect and Filter Misleading Audio Segments in Classroom Automatic Transcription

Hewstone,

Araya

2023

Applied Sciences

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Signal Processing Methods and Features for Enhancing a Speech Signal

Ganta,

Mittal,

Venkat Vijay Kishore

2023

Algorithms for Intelligent Systems

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Neural Networks-Based Approach for Detecting and Filtering Misleading Audio Segments for Classroom Automatic Transcription

Hewstone,

Araya

2023

Preprint

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Audio recording in classrooms is a common practice in educational research, with applications ranging from detecting classroom activities to analysing student behaviour. Previous research has employed neural networks for classroom activity detection and speaker role identification. However, these recordings are often affected by background noise that can hinder further analysis, and the literature has only sought to identify noise with general filters and not specifically designed for classrooms. Although the use of high-end microphones and environmental monitoring can mitigate this problem, these solutions can be costly and potentially disruptive to the natural classroom environment. In this context, we propose the development of a neural network model that can specifically detect and filter out background noise in classroom recordings. This model would allow the use of lower quality recordings without compromising analysis capability, thus facilitating data collection in natural educational environments and reducing the costs associated with high-end recording equipment.

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Training Based Noise Removal Technique for a Speech-to-Text Representation Model

Cited by 3 publications

References 9 publications

Neural Network-Based Approach to Detect and Filter Misleading Audio Segments in Classroom Automatic Transcription

Neural Network-Based Approach to Detect and Filter Misleading Audio Segments in Classroom Automatic Transcription

Signal Processing Methods and Features for Enhancing a Speech Signal

Neural Networks-Based Approach for Detecting and Filtering Misleading Audio Segments for Classroom Automatic Transcription

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