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In this research, we propose a parallel GAN-based scene classifier-based multi-angle audio speech recognition system. By taking into account a variety of audio sources and the matching visual situations, the system seeks to increase speech recognition accuracy. The suggested method aligns visual information with audio features by extracting visual features from various viewpoints and using a GAN-based scene classifier. The system then uses a parallel processing strategy to examine both the visual and aural aspects of the signals. The experimental findings demonstrate that the suggested system outperforms state-of-the-art techniques in terms of accuracy and efficiency. Applications for the proposed system include audio-visual scene analysis and voice recognition in noisy.
Keywords:
Index Terms-GAN, AVSR ,ASR,Voice recognition
Cite Article:
"Efficient Multi-angle Audio-visual Speech Recognition Using Parallel GAN based Scene Classifier", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 4, page no.163 - 167, April-2023, Available :http://www.ijrti.org/papers/IJRTI2304027.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator