Text to Speech Synthesis System for Punjabi language using Statistical Parametric Speech Synthesis Technique

Abstract

Statistical Parametric Speech Synthesis has been most growing technique rather than the traditional approaches that we are used to synthesizing the speech. The shortcoming of traditional approaches will be overcome with latest statistical techniques. The main advantages of SPSS from traditional synthesis technique are that it has more flexibility to change the characteristics of voice and support more multiple languages i.e. multilingual, has good coverage of acoustic ` and robustness. It generates high quality of speech from small training database. Deep Neural network and Hidden Morkov model are basic statistical parametric speech synthesis techniques. Gaussian mixture model, sinusoidal model are also under this categories. Features were extracted in two type spectral features like spectral bandwidth, spectral centroid etc. and excitation features like F0 frequencies etc. We are using 722 Punjabi phonemes. Using sound forge software we extracted the 200 wave file from 1 hour pre-recording wave file related to those phonemes. Each and every phonemes feature was extracted and saved in database. We were extracting 28 features of each phoneme. TTS text-to-speech system generates sounds or speech as a output when provided the text of Punjabi language. There were already many TTS are developed on different Indian languages. The system that we are trying to build is based only on Punjabi language.