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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 andfurther fine-tuned with studio-recorded TTS data to improve the timbre of thesynthesized voice. The motivation behind the work is that issues of word skipsand repetitions are usually absent in HMM systems due to their ability to modelthe duration distribution of phonemes accurately. Context-dependent pentaphonemodeling, along with tree-based clustering and state-tying, takes care ofunseen context and out-of-vocabulary words. A language model is also employedto reduce synthesis errors further. Subjective evaluations indicate that speechproduced using the proposed system is superior to the baseline E2E synthesisapproach in terms of intelligibility when combining complementing attributesfrom HMM and E2E frameworks. The further analysis highlights the proposedapproach's efficacy in low-resource scenarios.

HMM-based data augmentation for E2E systems for building conversational speech synthesis systems