Summary of Enhancing Multilingual Voice Toxicity Detection with Speech-text Alignment, by Joseph Liu et al.
Enhancing Multilingual Voice Toxicity Detection with Speech-Text Alignment
by Joseph Liu, Mahesh Kumar Nandwana, Janne Pylkkönen, Hannes Heikinheimo, Morgan McGuire
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); 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 A novel cross-modal learning framework integrates textual information into a multilabel speech toxicity classifier, enhancing its performance on large-scale datasets. By utilizing semantic text embeddings during training, the model learns to leverage relevant linguistic cues for toxicity classification. Experimental results demonstrate improvements in voice toxicity classification across five languages and different toxicity categories, highlighting the effectiveness of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to classify speech as toxic or not based on what’s being said. They combined two types of information – audio from the speech and text that describes it – to create a better model. This model can understand language and patterns in speech, which helps it accurately identify whether certain words or phrases are toxic. The team tested their approach with large datasets and found it worked well for five different languages. |
Keywords
* Artificial intelligence * Classification
