Greedy algorithms to optimize a sentence set near-uniformly distributed on syllable units and punctuation marks

Abstract

An optimum sentence set that near-uniformly dis- tributed on syllable units and punctuation marks is important to develop a syllable-based automatic speech recognition (ASR). It is usually extracted from a mother set of millions of unique sentences using Modified Least-to-Most (LTM) Greedy algorithm. The Modified LTM Greedy is capable of minimizing the number of syllables but ignores distributing their frequencies. Hence, two schemes are proposed to minimize the number of syllables as well as to distribute their frequencies near-uniformly. Testing on a mother set of 10 million Indonesian sentences shows that both schemes perform better than the Modified LTM Greedy for two syllable units: monosyllables and bisyllables.