  Automatic speech recognition (ASR) and Text to speech (TTS) are two prominent
area of research in human computer interaction nowadays. A set of phonetically
rich sentences is in a matter of importance in order to develop these two
interactive modules of HCI. Essentially, the set of phonetically rich sentences
has to cover all possible phone units distributed uniformly. Selecting such a
set from a big corpus with maintaining phonetic characteristic based similarity
is still a challenging problem. The major objective of this paper is to devise
a criteria in order to select a set of sentences encompassing all phonetic
aspects of a corpus with size as minimum as possible. First, this paper
presents a statistical analysis of Hindi phonetics by observing the structural
characteristics. Further a two stage algorithm is proposed to extract
phonetically rich sentences with a high variety of triphones from the EMILLE
Hindi corpus. The algorithm consists of a distance measuring criteria to select
a sentence in order to improve the triphone distribution. Moreover, a special
preprocessing method is proposed to score each triphone in terms of inverse
probability in order to fasten the algorithm. The results show that the
approach efficiently build uniformly distributed phonetically-rich corpus with
optimum number of sentences.
