A Speech Synthesizer for Persian Text Using a Neural Network with a Smooth Ergodic HMM

摘要

The feasibility of converting text into speech using an inexpensive computer with minimal memory is of great interest. Speech synthesizers have been developed for many popular languages (e.g., English, Chinese, Spanish, French, etc.), but designing a speech synthesizer for a language is largely dependant on the language structure. In this article, we develop a Persian synthesizer that includes an innovative text analyzer module. In the synthesizer, the text is segmented into words and after preprocessing, a neural network is passed over each word. In addition to preprocessing, a new model (SEHMM) is used as a postprocessor to compensate for errors generated by the neural network. The performance of the proposed model is verified and the intelligibility of the synthetic speech is assessed via listening tests.