  Unit selection synthesis systems required accurate segmentation and labeling
of the speech signal owing to the concatenative nature. Hidden Markov
model-based speech synthesis accommodates some transcription errors, but it was
later shown that accurate transcriptions yield highly intelligible speech with
smaller amounts of training data. With the arrival of end-to-end (E2E) systems,
it was observed that very good quality speech could be synthesised with large
amounts of data. As end-to-end synthesis progressed from Tacotron to
FastSpeech2, it has become imminent that features that represent prosody are
important for good-quality synthesis. In particular, durations of the sub-word
units are important. Variants of FastSpeech use a teacher model or forced
alignments to obtain good-quality synthesis. In this paper, we focus on
duration prediction, using signal processing cues in tandem with forced
alignment to produce accurate phone durations during training.
  The current work aims to highlight the importance of accurate alignments for
good-quality synthesis. An attempt is made to train the E2E systems with
accurately labeled data, and compare the same with approximately labeled data.
