Development of Small Footprint Korean Large Vocabulary Speech Recognition for Commanding a Standalone Robot

The work in this paper concerns a small footprint Acoustic Model (AM) and its use in the implementation of a Large Vocabulary Isolated Speech Recognition (LVISR) system for commanding a robot in the Korean language, which requires about 500KB of memory. Tree-based state clustering was applied to reduce the number of total unique states, while preserving its original performance. A decision tree induction method was developed for the tree-based state clustering. For this method, a binary question set, measurement function and stopping criterion were devised. A phoneme set consisting of 38 phonemes was defined for the implementation of small footprint Korean LVISR. Further reduction in memory requirement was achieved through integer arithmetic operation. The best multiplication factor was determined for this operation. As a result, we successfully developed a small footprint Korean LVISR that requires memory space about 500KB.