  Text-to-Speech (TTS) system is a system where speech is synthesized from a
given text following any particular approach. Concatenative synthesis, Hidden
Markov Model (HMM) based synthesis, Deep Learning (DL) based synthesis with
multiple building blocks, etc. are the main approaches for implementing a TTS
system. Here, we are presenting our deep learning-based end-to-end Bangla
speech synthesis system. It has been implemented with minimal human annotation
using only 3 major components (Encoder, Decoder, Post-processing net including
waveform synthesis). It does not require any frontend preprocessor and
Grapheme-to-Phoneme (G2P) converter. Our model has been trained with
phonetically balanced 20 hours of single speaker speech data. It has obtained a
3.79 Mean Opinion Score (MOS) on a scale of 5.0 as subjective evaluation and a
0.77 Perceptual Evaluation of Speech Quality(PESQ) score on a scale of [-0.5,
4.5] as objective evaluation. It is outperforming all existing non-commercial
state-of-the-art Bangla TTS systems based on naturalness.
