Hardware-Software Codesign of a Large Vocabulary Continuous Speech Recognition System

By Vivek Jayadev

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 hardwaresoftware
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
with the spectral analysis of the speech input, the second phase deals with
learning of the sound units followed by the recognition phase. Studies have
shown that the recognition phase consumes more than 50% of the processor
time. Keeping this in mind, we partitioned our design to perform the spectral
analysis and acoustic training in software using the front end executables and the
acoustic trainers provided by the CMU SPHINX. The decoder implementing the
phonetic detection and viterbi algorithm was designed in hardware. In this project
we simulated different speech input files in software and the relevant input vector
files required for hardware analysis were tapped from the SPHINX system.

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