Abstract
AbstractThis research paper investigated the robustness of the Automatic Visual Speech Recognition System (AVSR), for acoustic models that are based on GMM and DNNs. Most of the recent survey literature is surpassed in this article. Which shows how, over the last 30 years, analysis and product growth on AVSR robustness in noisy acoustic conditions has progressed? There are various categories of languages covered, with coverage, development processes, Corpus, and granularity varying. The key advantage of deep-learning tools, including a deeply convoluted neural network, a bi-directional long-term memory grid, a 3D convolutional neural network, and others, is that they are relatively easy to solve such problems, which are indissolubly linked to feature extraction and complex audiovisual fusion. Its objective is to act as an AVSR representative. KeywordsSpeech recognitionAVSRMFCCHMMCNNDNN
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
