Summary of Exploring Multilingual Unseen Speaker Emotion Recognition: Leveraging Co-attention Cues in Multitask Learning, by Arnav Goel et al.
Exploring Multilingual Unseen Speaker Emotion Recognition: Leveraging Co-Attention Cues in Multitask Learning
by Arnav Goel, Medha Hira, Anubha Gupta
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper presents a novel architecture called CAMuLeNet that addresses challenges in Speech Emotion Recognition (SER) for multilingual and unseen speakers. The model leverages co-attention based fusion and multitask learning to improve performance. The authors benchmark several pretrained encoders, including Whisper, HuBERT, Wav2Vec2.0, and WavLM, on five existing multilingual datasets and release a new dataset for SER in Hindi (BhavVani). CAMuLeNet shows an average improvement of approximately 8% over all benchmarks on unseen speakers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how computers can recognize emotions in people’s voices. It creates a new way to do this called CAMuLeNet that works well for people who speak different languages and aren’t familiar to the computer. The researchers tested several ways that computers already use to recognize emotions and found that their new method worked best. They also created a new set of voice recordings in Hindi that can be used to test how well computers do at recognizing emotions. |
Keywords
» Artificial intelligence » Attention
