Text-to-Speech Synthesis for Hindi Language Using MFCC and LPC Feature Extraction Techniques


  • Shaikh Naziya Sultana Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (MH), INDIA.
  • Ratnadeep R. Deshmukh Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (MH), INDIA.


Devanagari; Text-to-Speech (TTS); Hindi language; DCT; MFCC; LPC


India is a large country with over a billion populations who speak numerous languages. 43% of Indians speak Devanagari Hindi script, followed by Bengali, Telugu, Marathi, and other languages. The widespread generation of content and accessibility would therefore greatly benefit from text-to-speech systems for such languages. In this research work we improve the already available Text-to-Speech (TTS) system using advance preprocessing techniques to the Hindi corpus database and applied various feature extraction techniques for better result. Finally we got the accuracy as 98% using MFCC and LPC feature extraction techniques. The developed model is capable for getting the input from audio file and read it loudly using developed TTS system.


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How to Cite

Shaikh Naziya Sultana, & Ratnadeep R. Deshmukh. (2024). Text-to-Speech Synthesis for Hindi Language Using MFCC and LPC Feature Extraction Techniques. JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH, 6(3). Retrieved from http://joaasr.com/index.php/joaasr/article/view/943