Sharif Digital Repository

The goal of this research is to convert letter to phoneme using deep neural networks. As the deep neural networks are among the best methods for speech and text processing (The highest accuracy in converting text to letter in English language is obtained by using deep neural networks too.), multilayer deep neural networks are used in this research to increase the accuracy. It should be noted that deep neural networks have not been used for converting text to phoneme in Persian language before. In this research a rule based alignment method based on our preposed rule is presented and achieved an accuracy more than 98%. Several approaches for converting word to grapheme with emphasis on the... 

Many Text-to-Speech (TTS) systems, particularly in low-resource environments, struggle to produce natural and intelligible speech from grapheme sequences. One solution to this problem is to use Grapheme-to-Phoneme (G2P) conversion to increase the information in the input sequence and improve the TTS output. However, current G2P systems are not accurate or efficient enough for Persian texts due to the language’s complexity and the lack of short vowels in Persian grapheme sequences. In our study, we aimed to improve resources for the Persian language. To achieve this, we introduced two new G2P training datasets, one manually-labeled and the other machine-generated, containing over five million...