  A Spoken dialogue system for an unseen language is referred to as Zero
resource speech. It is especially beneficial for developing applications for
languages that have low digital resources. Zero resource speech synthesis is
the task of building text-to-speech (TTS) models in the absence of
transcriptions. In this work, speech is modelled as a sequence of transient and
steady-state acoustic units, and a unique set of acoustic units is discovered
by iterative training. Using the acoustic unit sequence, TTS models are
trained. The main goal of this work is to improve the synthesis quality of zero
resource TTS system. Four different systems are proposed. All the systems
consist of three stages: unit discovery, followed by unit sequence to
spectrogram mapping, and finally spectrogram to speech inversion. Modifications
are proposed to the spectrogram mapping stage. These modifications include
training the mapping on voice data, using x-vectors to improve the mapping,
two-stage learning, and gender-specific modelling. Evaluation of the proposed
systems in the Zerospeech 2020 challenge shows that quite good quality
synthesis can be achieved.
