LAUSR

In the paper, we proposed a method of constructing a language corpus based on the imitation of abbreviated and transformed particles that are distinctive feature of Korean spontaneous spoken language. Since it is not practical to train a spoken-style model using numerous spoken transcripts, the proposed approach generates a spoken-style text from a written-style one such as newspapers, based on characteristics of pronouncing variations, dependent on spoken styles, of typical particles. This method for constructing spoken-style text is based on statistical analysis on particles that play same function in both of written and spoken language. We analyze grammatical functions and pronouncing features of particles that distinguish between written and spoken language, and generate spoken-style text from written-style text by imitating typical abbreviated and transformed particles which play same function. Abbreviated and transformed particles to be imitated have proper and typical pronouncing features of spoken language. We replace particles with abbreviated and transformed particles in written-style text according to correspondence of written particles to spoken ones, which results in spoken-style text. The language model, which is trained from spoken-style text imitating abbreviated and transformed particles, significantly improved a word error rate (WER) on spontaneous speech.