A letter to sound system for Farsi language using neural networks

Construction of letter to sound (LTS) conversion systems in Farsi language is a difficult task, and because of the omission of some vowels in Farsi orthography, these systems in general have low efficiencies. In this paper, the structure of a letter to sound system, having a three-layers architecture, has been presented. The first layer is rule-based and the second layer consists of five multi layer perceptron (MLP) neural networks and a controller section for pronunciations determination. The third layer has a MLP network for detection of geminated letters by using results obtained from the previous steps. The proposed system is designed to produce rational pronunciations for every word, where the rational pronunciation means a phonetic transcription which follows the correct Farsi syllabification structure and the obvious rules of phonetics. The authors have achieved 87% and 61% correct word and letter to sound conversion, performance respectively which is quite satisfactory for a Farsi language LTS system.