Summary of Usefulness Of Emotional Prosody in Neural Machine Translation, by Charles Brazier and Jean-luc Rouas
Usefulness of Emotional Prosody in Neural Machine Translation
by Charles Brazier, Jean-Luc Rouas
First submitted to arxiv on: 27 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This neural machine translation (NMT) paper proposes a novel approach to improve translation quality by incorporating automatically recognized emotions in voice recordings. The method uses a state-of-the-art speech emotion recognition (SER) model to predict dimensional emotion values from input audio, which are then used as source tokens added at the beginning of input texts to train an NMT model. Experimental results show that integrating emotion information, particularly arousal, into NMT systems leads to better translations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special machines to help translate languages better. It’s like when you’re talking to someone and you can tell how they’re feeling just by listening to their voice. The researchers want to use this idea to make language translation more accurate. They do this by training a machine learning model on audio recordings of people speaking, and then using that information to help translate text from one language to another. It seems like it makes the translations better! |
Keywords
» Artificial intelligence » Machine learning » Translation
