Editing Multimodal Speech Recognition (section)

=== Robustness in Noisy Environments ===
One of the significant challenges in automatic speech recognition (ASR) is its performance degradation in noisy environments. Traditional ASR systems excel in quiet conditions but struggle when confronted with background noise. However, Several researches have shown that multimodal speech recognition systems perform better over single-modal speech recognition systems  in noisy environments<ref>Chibelushi, C. C. (1996). Design issues for a digital audio-visual integrated database. ''IEE Colloquium on Integrated Audio-Visual Processing for Recognition, Synthesis and Communication'', ''1996'', 7–7. <nowiki>https://doi.org/10.1049/ic:19961151</nowiki></ref><ref>Stewart, D., Seymour, R., Pass, A., & Ji Ming. (2014). Robust Audio-Visual Speech Recognition Under Noisy Audio-Video Conditions. ''IEEE Transactions on Cybernetics'', ''44''(2), 175–184. <nowiki>https://doi.org/10.1109/TCYB.2013.2250954</nowiki></ref><ref>Kashiwagi, Y., Suzuki, M., Minematsu, N., & Hirose, K. (2012). Audio-visual feature integration based on piecewise linear transformation for noise robust automatic speech recognition. ''2012 IEEE Spoken Language Technology Workshop (SLT)'', 149–152. <nowiki>https://doi.org/10.1109/SLT.2012.6424213</nowiki></ref>.This innovation provides an essential breakthrough in ensuring accurate speech recognition across various real-world scenarios, where noise interference is a common occurrence.