Context dependent statistical augmentation of persian transcripts

Persian language is transcribed in a lossy manner as it does not, as a rule, encode vowel information. This renders the use of the written script suboptimal for language models for speech applications or for statistical machine translation. It also causes the text-to-speech synthesis from a Persian script input to be a one-to-many operation. In our previous work, we introduced an augmented transcription scheme that eliminates the ambiguity present in the Arabic script. In this paper, we propose a method of generating the augmented transcription from the Arabic script by statistically decoding through all possibilities and choosing the maximum likelihood solution. We demonstrate that even with a small amount of initial bootstrap data, we can achieve a decoding precision of about 93% with no human intervention. The precision can be as high as 99.2% in a semi-automated mode where low confidence decisions are marked for human processing.