噪声鲁棒语音情感识别与信噪比自适应语音增强
Noise robust speech emotion recognition with signal-to-noise ratio adapting speech enhancement
AI总结：
提出NRSER系统，结合语音增强与信噪比级别检测，提升语音情感识别在噪声环境下的鲁棒性，并防止对纯噪声信号做出情感预测。
摘要：
语音情感识别（SER）常因背景噪声而性能下降。此外，对仅含背景噪声的信号进行预测可能削弱用户对系统的信任。在本研究中，我们提出了一种噪声鲁棒语音情感识别系统NRSER。NRSER采用语音增强（SE）有效降低输入信号中的噪声。随后，引入信噪比（SNR）级别检测结构和波形重构策略，以减少SE对无或少量背景噪声的语音信号的负面影响。我们的实验结果表明，NRSER能有效提升SER系统的噪声鲁棒性，包括防止系统对仅由背景噪声组成的信号进行情感识别。此外，所提出的SNR级别检测结构可单独用于数据选择等任务。
英文摘要：
Speech emotion recognition (SER) often experiences reduced performance due to background noise. In addition, making a prediction on signals with only background noise could undermine user trust in the system. In this study, we propose a Noise Robust Speech Emotion Recognition system, NRSER. NRSER employs speech enhancement (SE) to effectively reduce the noise in input signals. Then, the signal-to-noise-ratio (SNR)-level detection structure and waveform reconstitution strategy are introduced to reduce the negative impact of SE on speech signals with no or little background noise. Our experimental results show that NRSER can effectively improve the noise robustness of the SER system, including preventing the system from making emotion recognition on signals consisting solely of background noise. Moreover, the proposed SNR-level detection structure can be used individually for tasks such as data selection.
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