PCQ: Emotion Recognition in Speech via Progressive Channel Querying
<|reference_start|>PCQ: Emotion Recognition in Speech via Progressive Channel Querying: In human-computer interaction (HCI), Speech Emotion Recognition (SER) is a key technology for understanding human intentions and emotions. Traditional SER methods struggle to effectively capture the long-term temporal correla-tions and dynamic variations in complex emotional expressions. To overcome these limitations, we introduce the PCQ method, a pioneering approach for SER via \textbf{P}rogressive \textbf{C}hannel \textbf{Q}uerying. This method can drill down layer by layer in the channel dimension through the channel query technique to achieve dynamic modeling of long-term contextual information of emotions. This mul-ti-level analysis gives the PCQ method an edge in capturing the nuances of hu-man emotions. Experimental results show that our model improves the weighted average (WA) accuracy by 3.98\% and 3.45\% and the unweighted av-erage (UA) accuracy by 5.67\% and 5.83\% on the IEMOCAP and EMODB emotion recognition datasets, respectively, significantly exceeding the baseline levels.<|reference_end|>
@article{wang2024pcq:,
    title={PCQ: Emotion Recognition in Speech via Progressive Channel Querying},
    author={Xincheng Wang and Liejun Wang and Yinfeng Yu and Xinxin Jiao},
    journal={arXiv preprint arXiv:2407.12380},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12380},
    primaryClass={eess.AS cs.SD}
}