Sunan Li
Biographic Data
| ID | 6222869 |
|---|---|
| NAME | Sunan Li |
| GIVEN NAMES | Sunan |
| FAMILY NAME | Li |
| SIGNATURE | LI S |
| AFFILIATIONS | Southeast University |
| ORCID | 0000-0003-1494-4873 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
An Interpretable Collaborative Acoustic Parameter Modeling Network for Speech Emotion Recognition
Acoustic parameters can collaboratively represent speech emotions. However, existing speech emotion recognition (SER) methods neglect explicit collaborative modeling among acoustic parameters during representation learning. This not only limits their ability to extract the information contained in acoustic parameters that collaboratively represent emotions, but also makes it difficult to trace back which acoustic parameters are closely associated…
Preliminary exploration of the antidepressant mechanism of Bupleurum scorzonerifolium-Scutellaria baicalensis and its core SSa-Baicalin combination
Speech Emotion Recognition via an Attentive Time–Frequency Neural Network
Spectrogram is commonly used as the input feature of deep neural networks to learn the high(er)-level time–frequency pattern of speech signal for speech emotion recognition (SER). Generally, different emotions correspond to specific energy activations both within frequency bands and time frames on spectrogram, which indicates the frequency and time domains are both essential to represent the emotion for SER. However, recent spectrogram-based work…
No prominent works on this page.
Speech Emotion Recognition via an Attentive Time–Frequency Neural Network
Spectrogram is commonly used as the input feature of deep neural networks to learn the high(er)-level time–frequency pattern of speech signal for speech emotion recognition (SER). Generally, different emotions correspond to specific energy activations both within frequency bands and time frames on spectrogram, which indicates the frequency and time domains are both essential to represent the emotion for SER. However, recent spectrogram-based work…
Preliminary exploration of the antidepressant mechanism of Bupleurum scorzonerifolium-Scutellaria baicalensis and its core SSa-Baicalin combination
An Interpretable Collaborative Acoustic Parameter Modeling Network for Speech Emotion Recognition
Acoustic parameters can collaboratively represent speech emotions. However, existing speech emotion recognition (SER) methods neglect explicit collaborative modeling among acoustic parameters during representation learning. This not only limits their ability to extract the information contained in acoustic parameters that collaboratively represent emotions, but also makes it difficult to trace back which acoustic parameters are closely associated…
Artificial neural network (2 works) · Emotion and Mood Recognition (2 works) · Music and Audio Processing (2 works) · Alternative medicine (1 work) · Antidepressant (1 work) · Artificial Intelligence (1 work) · Autoencoder (1 work) · Baicalin (1 work) · Biochemical effects in animals (1 work) · Chemistry (1 work)