Editing Hidden Markov Models in Speech Synthesis (section)

== Introduction ==
Hidden Markov Model (HMM)-based speech synthesis is a remarkably effective technique in synthesizing speech. The most attractive part of HTS system is that speaker identities, speaking styles, or emotions can easily be modified by transforming HMM parameters using various techniques such as adaptation, interpolation, Eigen voice, Or multiple Regression. 

In the realm of Text-to-speech (TTS) synthesis, the main goal is to transform input text into intelligible and natural sounding speech. The TTS components involves the two phases, the front end and the back end. The front end analyses the text, creates possible pronunciations for each word in the context with grapheme to phoneme conversion. The back end generates the speech waveform along with the prosody of the sentence to be spoken. The evaluation of TTS system is based on three critical attributes: accuracy, intelligibility, and naturalness. 

The HTS system provides the frequency spectrum (Vocal tract), fundamental frequency (vocal source) and duration (Prosody) of speech, which are statistically generated by using HMM based on maximum likelihood criterion. HTS system is an open source tool which provides a research and development platform for statistical parametric speech synthesis. The HMM-based speech synthesis system (HTS) has been developed by the HTS working group as an extension of the HMM toolkit (HTK)<ref>Kayte, S., Mundada, M., & Gujrathi, J. (2015). Hidden Markov model based speech synthesis: A review. ''International Journal of Computer Applications'', ''130''(3), 35-39.</ref>.