Since the mental states of the speaker modulate speech, stress introduced by cognitive or physical loads could be detected in the voice. The existing voice stress detection benchmark has shown that the audio embeddings extracted from the Hybrid BYOL-S self-supervised model perform well. However, the benchmark only evaluates performance separately on each dataset, but does not evaluate performance across the different types of stress and different languages. Moreover, previous studies found strong individual differences in stress susceptibility. This paper presents the design and development of voice stress detection, trained on more than 100 speakers from 9 language groups and five different types of stress. We address individual variabilities in voice stress analysis by adding speaker embeddings to the hybrid BYOL-S features. The proposed method significantly improves voice stress detection performance with an input audio length of only 3-5 seconds.

翻译：由于说话人的心理状态会调制语音，认知或生理负荷引起的压力可通过声音检测。现有语音压力检测基准表明，从混合BYOL-S自监督模型中提取的音频嵌入表现良好。然而，该基准仅在各数据集上单独评估性能，并未评估不同压力类型和不同语言间的跨域表现。此外，先前研究发现压力敏感性存在显著的个体差异。本文介绍了基于9个语言组、100多名说话人及五种压力类型的语音压力检测系统设计与开发。通过在混合BYOL-S特征中添加说话人嵌入，我们解决了语音压力分析中的个体差异问题。所提方法在仅需3-5秒输入音频长度的情况下，显著提升了语音压力检测性能。