Auto Regressive Moving Average Model Base Speech Synthesis for Phoneme Transitions
نویسندگان
چکیده
Speech synthesizers based on paramedic methods, still have not achieved the expected naturalness. This is due to less consideration on linear time variant nature between the neighbor phonemes. This paper presents a study to model the phoneme transitions between neighbor phonemes with lesser number of parameters using Auto Regressive Moving Average (ARMA) model, where Steiglitz-McBride algorithm is used to estimate the zeros and poles of the system. The results are compared with an Auto Regressive (AR) model, which show that the correlation between the source signal and the reconstructed signal in ARMA model is higher than that of the AR model.
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