Abstract
Artificial Neural Network (ANN) based recognition systems show dependence on data and hardware for achieving better performance. The work here describes the use of DSP processors to design a bio-inspired soft-computational framework with which processing of speech and image inputs are carried out. Certain nonlinear activation function for implementation in DSP processor framework is also designed and configured appropriately to train a soft-computational tool like ANN. The results derived show that the capability of the ANN improves with the derived DSP processor framework. Its performance is further enhanced using the approximation of tan-sigmoidal nonlinear activation function. In terms of computational capability, the proposed approach shows around 12 \(\%\) improvement compared to a conventional framework. Similarly, improvement in recognition rate is around 4 \(\%\) with applications involving speech and image samples.
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Sarma, D., Sarma, K.K. (2013). Bio-Inspired Soft-Computational Framework for Speech and Image Application. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 201. Springer, India. https://doi.org/10.1007/978-81-322-1038-2_5
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DOI: https://doi.org/10.1007/978-81-322-1038-2_5
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