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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(More)
-In this paper, we use a two-dimensional (2-D) AR model for texture description. The coefficients of the AR model as the parameters can thus be used to identify textured images. These processes are ideally suited to implementation by neural networks which are well known for their parallel execution and adaptive learning abilities. The proposed network(More)
This paper proposes a FPGA design of variable parameter interleaver based on the time-domain convolutional interleaver. Synchronous Dynamic Random Access Memory (SDRAM) is adopted in the design. According to different interleave-depth and configuration parameters, the memory of SDRAM is assigned dynamically. The result of the simulation is shown at the end(More)
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