Read it to me: An emotionally aware Speech Narration Application
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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.
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
6 of 11 references.
References · 6
- 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
- 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 804
- 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 622
- 2018Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesiscited by 282
- 2020Controllable Emotion Transfer For End-to-End Speech Synthesiscited by 32
- 2019MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long samples using Spectrogramscited by 12
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