Its applications span across disciplines such as computer vision, natural language processing, search engines, medical diagnosis, classification of DNA sequences, speech recognition, computational finance, fraud detection, and many others.
The kernel trick has been applied successfully in a variety of different fields, including natural language processing, face recognition, speech recognition and computational biology.
As a kid, Mercer was taken with computers, and in the 1980s he joined an IBM team that also included Brown, pioneering the field of computational linguistics, the foundation of speech recognition and computer translation.
Experimental results show that unnormalized probability of NNLM is quite complementary to that of back-off N-gram, and combining the unnormalized NNLM and back-off N-gram can further improve the performance of speech recognition with little computational resource.
This algorithm unifies the nonlinear frequency warping function and speaker variability due to vocal tract length differences in the front-end of the automatic speech recognition and significantly reduces computational complexity.
In the design of a practical robust speech recognition system for ambient intelligence, computational complexity is a very important factor.
Sparse Dynamic Programming has emerged as an essential tool for the design of efficient algorithms for optimization problems coming from such diverse areas as computer science, computational biology, and speech recognition.
Only few studies so far have attempted to use this cortical representation in speech processing, yielding some improvements for automatic speech recognition at the expense of substantial computational complexity[4, 5].
Kepler on mobile can also offer this, which means that mobile apps featuring things like computational imaging, computer vision, AR or speech recognition would be able to benefit from the Kepler GPU and take advantage not only of its processing power, but also its power efficiency.
Balancing performance and computational cost for robust speech recognition for ambient intelligence will be a design art.
This paper deals with the issue of speech recognition in situations of limited memory resource and limited computational cost.
