# Using Deepfake Technologies for Word Emphasis Detection

arXiv: https://arxiv.org/abs/2305.07791 · HTML: https://mlsanity.com/paper/2305.07791/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 15 of 15)

- [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)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://mlsanity.com/paper/1806.04558/) (2018)
- [Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://mlsanity.com/paper/1803.09017/) (2018)
- [Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron](https://mlsanity.com/paper/1803.09047/) (2018)
- [Hierarchical Generative Modeling for Controllable Speech Synthesis](https://mlsanity.com/paper/1810.07217/) (2018)
- [Deep Voice 2: Multi-Speaker Neural Text-to-Speech](https://mlsanity.com/paper/1705.08947/) (2017)
- [Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech](https://mlsanity.com/paper/2005.05106/) (2020)
- [Learning latent representations for style control and transfer in end-to-end speech synthesis](https://mlsanity.com/paper/1812.04342/) (2018)
- [Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTS](https://mlsanity.com/paper/1904.04764/) (2019)
- [Prosodic Clustering for Phoneme-level Prosody Control in End-to-End Speech Synthesis](https://mlsanity.com/paper/2111.10177/) (2021)
- [Syntactic representation learning for neural network based TTS with syntactic parse tree traversal](https://mlsanity.com/paper/2012.06971/) (2020)
- [A Study of Modeling Rising Intonation in Cantonese Neural Speech Synthesis](https://mlsanity.com/paper/2208.02189/) (2022)

## Abstract (arXiv)

In this work, we consider the task of automated emphasis detection for spoken language. This problem is challenging in that emphasis is affected by the particularities of speech of the subject, for example the subject accent, dialect or voice. To address this task, we propose to utilize deep fake technology to produce an emphasis devoid speech for this speaker. This requires extracting the text of the spoken voice, and then using a voice sample from the same speaker to produce emphasis devoid speech for this task. By comparing the generated speech with the spoken voice, we are able to isolate patterns of emphasis which are relatively easy to detect.