# Read it to me: An emotionally aware Speech Narration Application

arXiv: https://arxiv.org/abs/2209.02785 · HTML: https://mlsanity.com/paper/2209.02785/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [WaveGrad: Estimating Gradients for Waveform Generation](https://mlsanity.com/paper/2009.00713/) (2020)
- [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://mlsanity.com/paper/1806.04558/) (2018)
- [Controllable Emotion Transfer For End-to-End Speech Synthesis](https://mlsanity.com/paper/2011.08679/) (2020)
- [MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long samples using Spectrograms](https://mlsanity.com/paper/1910.03713/) (2019)

## Abstract (arXiv)

In this work we try to perform emotional style transfer on audios. In particular, MelGAN-VC architecture is explored for various emotion-pair transfers. The generated audio is then classified using an LSTM-based emotion classifier for audio. We find that "sad" audio is generated well as compared to "happy" or "anger" as people have similar expressions of sadness.