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In lexical model design language plays a major role. Each language has its own rhythm in speech and language aspects. Based on language rhythm worldly languages are classified as stress timed and syllable timed rhythm. Most of ASR systems use the lexical model that is built for stressed timed languages. All Indian languages are syllable timed rhythmic languages one such is Telugu Language. In this paper analysis of the decoding results of ASR system using two different lexical model environments. One is CMU lexicon which is based on stress timed language as the tool is used American accent English phonemes and another UOH lexicon which is handcraft lexicon for Telugu language which is also a syllable timed language.. Further studied are the gender and accents (pronunciation variant factors) effecting the Substitutional errors in ASR system. The confusion matrix for vowel and consonants alone analyzed for both cases and also for isolated word recognition where the confusion matrix gives the most common phonemes substituted. In all the cases the UOH lexicon based ASR system gives the improvement of word accuracy around 20 to 30%.Speech is a process used to communicate from a speaker to listener. Pronunciation relates to speech, and humans have an intuitive feel for pronunciation. For instance, people chuckle when words are mispronounced and notice when foreign accent colors a speaker's pronunciations. [1]. If the words were always pronounced in the same way, ASR would be relatively easy. However, for various reasons words are almost always pronounced differently and varied from one speaker to another and from once situation to another. The variability is due to co-articulation, reasonal accents, speaking rate, speaking style etc. I.Language need in ASR study:There are 6912 languages are there the purpose of communication between human being around the world. The need of the language is to computerization of many human need domains, Ubiquitous information access, phone-based information access, mobile devices which demand speech as modality to interact, globalization in cross-cultural human-human interaction, multilingual communities, Humanitarian needs like disaster, healthcare, military applications to communicate with local people and main focus on Human machine interaction where people expect speech-driven applications in their mother tongue will specifically demand the speech recognition research to work in regional language . such one application here we like to develop a speech recognition system working in Telugu Language.

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