  Vocoders received renewed attention as main components in statistical
parametric text-to-speech (TTS) synthesis and speech transformation systems.
Even though there are vocoding techniques give almost accepted synthesized
speech, their high computational complexity and irregular structures are still
considered challenging concerns, which yield a variety of voice quality
degradation. Therefore, this paper presents new techniques in a continuous
vocoder, that is all features are continuous and presents a flexible speech
synthesis system. First, a new continuous noise masking based on the phase
distortion is proposed to eliminate the perceptual impact of the residual noise
and letting an accurate reconstruction of noise characteristics. Second, we
addressed the need of neural sequence to sequence modeling approach for the
task of TTS based on recurrent networks. Bidirectional long short-term memory
(LSTM) and gated recurrent unit (GRU) are studied and applied to model
continuous parameters for more natural-sounding like a human. The evaluation
results proved that the proposed model achieves the state-of-the-art
performance of the speech synthesis compared with the other traditional
methods.
