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Using Deepfake Technologies for Word Emphasis Detection

arXiv 2305.07791 ↗First posted 12 May 2023cs.LG cs.AIcited by 0

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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.

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.

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References · 15

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  10. 2020Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speechcited by 63
  11. 2018Learning latent representations for style control and transfer in end-to-end speech synthesiscited by 61
  12. 2019Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTScited by 10
3 more
  1. 2021Prosodic Clustering for Phoneme-level Prosody Control in End-to-End Speech Synthesiscited by 8
  2. 2020Syntactic representation learning for neural network based TTS with syntactic parse tree traversalcited by 4
  3. 2022A Study of Modeling Rising Intonation in Cantonese Neural Speech Synthesiscited by 2

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