Visual speech recognition by recurrent neural networks

Gihad Rabi; Si Wei Lu

doi:10.1117/1.482627

1 January 1998 Visual speech recognition by recurrent neural networks

Gihad Rabi, Si Wei Lu

Journal of Electronic Imaging, Vol. 7, Issue 1, (January 1998). https://doi.org/10.1117/1.482627

One of the major drawbacks of current acoustically based speech recognizers is that their performance deteriorates drastically with noise. Our focus is to develop a computer system that performs speech recognition based on visual information concerning the speaker. The system automatically extracts visual speech features through image-processing techniques that operate on facial images taken in a normally illuminated environment. To cope with the dynamic nature of change in speech patterns with respect to time as well as the spatial variations in the individual patterns, the proposed recognition scheme uses a recurrent neural network architecture. By specifying a certain behavior when the network is presented with exemplar sequences, the recurrent network is trained with no more than feedforward complexity. The network’s desired behavior is based on characterizing a given word by well-defined segments. Adaptive segmentation is employed to segment the training sequences of a given class. This technique iterates the execution of two steps. First, the sequences are segmented individually. Then, a generalized version of dynamic time warping is used to align the segments of all sequences. At each iteration, the weights of the distance functions used in the two steps are updated in a way that minimizes a segmentation error. The system is implemented and tested on a few words. The results are satisfactory. In particular, the system is able to distinguish between words with common segments. Moreover, it tolerates to a great extent variable-duration words of the same class.

Citation Download Citation

Gihad Rabi and Si Wei Lu "Visual speech recognition by recurrent neural networks," Journal of Electronic Imaging 7(1), (1 January 1998). https://doi.org/10.1117/1.482627

Published: 1 January 1998

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available