Abstract: In developing a text-to-speech system, it is well
known that the accuracy of information extracted from a text is
crucial to produce high quality synthesized speech. In this paper, a
new scheme for converting text into its equivalent phonetic spelling
is introduced and developed. This method is applicable to many
applications in text to speech converting systems and has many
advantages over other methods. The proposed method can also
complement the other methods with a purpose of improving their
performance. The proposed method is a probabilistic model and is
based on Smooth Ergodic Hidden Markov Model. This model can be
considered as an extension to HMM. The proposed method is applied
to Persian language and its accuracy in converting text to speech
phonetics is evaluated using simulations.