Audio-visual speech recognition based on machine learning approach Online publication date: Tue, 12-Apr-2022
by Saswati Debnath; Pinki Roy
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 21, No. 3/4, 2022
Abstract: Audio-visual speech recognition by machine plays an important role when research in automatic speech recognition reaches its highest performance. Audio alone also gives good performance, but adding the visual information potentially gives more convenient recognition system when an audio signal degrades in a noisy environment and may vary because of the environmental channel. This paper proposes an audio-visual automatic speech recognition (AV-ASR) system based on machine learning approaches. Visual information is captured from lip contour. Pseudo Zernike moments (PZMs) and 19th order Mel frequency cepstral coefficients (MFCCs) are extracted to obtain visual information and audio feature respectively. Machine learning approach, artificial neural networks (ANN) and support vector machines (SVM) are used to recognise speech for audio and visual modality. After the individual recognition of two systems, a combined decision is taken. This paper also evaluates the individual performance of both audio and visual speech recognition by machine learning approach.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook