Abstract

Modern real-time applications with increasing design complexity have revolutionized the embedded design procedure. Energy budget constraints and shortening time to market have led designers to consider cooperative design of hardware and software modules for a given embedded application. In hardware-software codesign the trade offs in both the domains are carefully analyzed and the processor intensive tasks are off-loaded to the hardware to meet the performance criteria while the rest is implemented in software to provide the required features and flexibility. Speech recognition systems used in real time applications involve complex algorithms for faithful recognition. The nature of these tasks restricts the implementation to large platforms and is not feasible to meet the performance constraints for smaller embedded mobile systems and battery operated devices. This thesis proposes an idea for hardware-software codesign of a Hidden Markov Model (HMM) based large vocabulary continuous speech recognition system. The entire procedure can be divided into three phases: the initial phase deals

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