  We propose Jointly trained Duration Informed Transformer (JDI-T), a
feed-forward Transformer with a duration predictor jointly trained without
explicit alignments in order to generate an acoustic feature sequence from an
input text. In this work, inspired by the recent success of the duration
informed networks such as FastSpeech and DurIAN, we further simplify its
sequential, two-stage training pipeline to a single-stage training.
Specifically, we extract the phoneme duration from the autoregressive
Transformer on the fly during the joint training instead of pretraining the
autoregressive model and using it as a phoneme duration extractor. To our best
knowledge, it is the first implementation to jointly train the feed-forward
Transformer without relying on a pre-trained phoneme duration extractor in a
single training pipeline. We evaluate the effectiveness of the proposed model
on the publicly available Korean Single speaker Speech (KSS) dataset compared
to the baseline text-to-speech (TTS) models trained by ESPnet-TTS.
