International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 130 - Number 3 |
Year of Publication: 2015 |
Authors: Sangramsing Kayte, Monica Mundada, Jayesh Gujrathi |
10.5120/ijca2015906965 |
Sangramsing Kayte, Monica Mundada, Jayesh Gujrathi . Hidden Markov Model based Speech Synthesis: A Review. International Journal of Computer Applications. 130, 3 ( November 2015), 35-39. DOI=10.5120/ijca2015906965
A Text-to-speech (TTS) synthesis system is the artificial production of human system. This paper reviews recent research advances in field of speech synthesis with related to statistical parametric approach to speech synthesis based on HMM. In this approach, Hidden Markov Model based Text to speech synthesis (HTS) is reviewed in brief. The HTS is based on the generation of an optimal parameter sequence from subword HMMs. The quality of HTS framework relies on the accurate description of the phoneset. The most attractive part of HTS system is the prosodic characteristics of the voice can be modified by simply varying the HMM parameters, thus reducing the large storage requirement.