  This paper proposes an approach to build a high-quality text-to-speech (TTS)
system for technical domains using data augmentation. An end-to-end (E2E)
system is trained on hidden Markov model (HMM) based synthesized speech and
further fine-tuned with studio-recorded TTS data to improve the timbre of the
synthesized voice. The motivation behind the work is that issues of word skips
and repetitions are usually absent in HMM systems due to their ability to model
the duration distribution of phonemes accurately. Context-dependent pentaphone
modeling, along with tree-based clustering and state-tying, takes care of
unseen context and out-of-vocabulary words. A language model is also employed
to reduce synthesis errors further. Subjective evaluations indicate that speech
produced using the proposed system is superior to the baseline E2E synthesis
approach in terms of intelligibility when combining complementing attributes
from HMM and E2E frameworks. The further analysis highlights the proposed
approach's efficacy in low-resource scenarios.
