  End-to-end text-to-speech synthesis systems achieved immense success in
recent times, with improved naturalness and intelligibility. However, the
end-to-end models, which primarily depend on the attention-based alignment, do
not offer an explicit provision to modify/incorporate the desired prosody while
synthesizing the signal. Moreover, the state-of-the-art end-to-end systems use
autoregressive models for synthesis, making the prediction sequential. Hence,
the inference time and the computational complexity are quite high. This paper
proposes Prosody-TTS, an end-to-end speech synthesis model that combines the
advantages of statistical parametric models and end-to-end neural network
models. It also has a provision to modify or incorporate the desired prosody by
controlling the fundamental frequency (f0) and the phone duration. Generating
speech samples with appropriate prosody and rhythm helps in improving the
naturalness of the synthesized speech. We explicitly model the duration of the
phoneme and the f0 to have control over them during the synthesis. The model is
trained in an end-to-end fashion to directly generate the speech waveform from
the input text, which in turn depends on the auxiliary subtasks of predicting
the phoneme duration, f0, and mel spectrogram. Experiments on the Telugu
language data of the IndicTTS database show that the proposed Prosody-TTS model
achieves state-of-the-art performance with a mean opinion score of 4.08, with a
very low inference time.
