Summary of Improvement and Implementation Of a Speech Emotion Recognition Model Based on Dual-layer Lstm, by Xiaoran Yang et al.
Improvement and Implementation of a Speech Emotion Recognition Model Based on Dual-Layer LSTM
by Xiaoran Yang, Shuhan Yu, Wenxi Xu
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 enhances an existing speech emotion recognition model by adding an additional LSTM layer to improve accuracy and processing efficiency. The modified dual-layer LSTM network captures long-term dependencies within audio sequences, allowing for more accurate classification of complex emotional patterns. Experiments on the RAVDESS dataset show a 2% improvement in accuracy compared to the single-layer LSTM, along with reduced recognition latency. This architecture is well-suited for handling emotional features with long-term dependencies and provides an optimization for speech emotion recognition systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a model better at recognizing emotions in audio recordings by adding more layers. The new model does this job 2% better than the old one, while also being faster. This is important because it can help create better customer service robots and tools that understand people’s feelings. |
Keywords
» Artificial intelligence » Classification » Lstm » Optimization
