  The performance of text-to-speech (TTS) systems heavily depends on
spectrogram to waveform generation, also known as the speech reconstruction
phase. The time required for the same is known as synthesis delay. In this
paper, an approach to reduce speech synthesis delay has been proposed. It aims
to enhance the TTS systems for real-time applications such as digital
assistants, mobile phones, embedded devices, etc. The proposed approach applies
Fast Griffin Lim Algorithm (FGLA) instead Griffin Lim algorithm (GLA) as
vocoder in the speech synthesis phase. GLA and FGLA are both iterative, but the
convergence rate of FGLA is faster than GLA. The proposed approach is tested on
LJSpeech, Blizzard and Tatoeba datasets and the results for FGLA are compared
against GLA and neural Generative Adversarial Network (GAN) based vocoder. The
performance is evaluated based on synthesis delay and speech quality. A 36.58%
reduction in speech synthesis delay has been observed. The quality of the
output speech has improved, which is advocated by higher Mean opinion scores
(MOS) and faster convergence with FGLA as opposed to GLA.
