基于神经元嵌入的发声疲劳检测
Detecting Vocal Fatigue with Neural Embeddings
摘要：
嗓音疲劳是指由于长时间使用嗓音而产生的声音疲劳和虚弱的感觉。本文研究了神经嵌入用于检测声音疲劳的有效性。我们在学术英语口语语料库上比较了x-vector、ECAPA-TDNN和wav 2 vec 2.0嵌入。低维映射的数据显示，神经嵌入捕获的信息在长时间的语音使用过程中的扬声器的声音特性的变化。我们表明，语音疲劳可以可靠地预测使用所有三种神经嵌入后，只有50分钟的连续发言时，时间平滑和归一化应用到提取的嵌入。我们采用支持向量机进行分类，并使用x向量实现了81%的准确率，使用ECAPA-TDNN嵌入实现了85%的准确率，使用wav 2 vec 2.0嵌入实现了82%的准确率。我们得到了76%的准确率得分，当训练后的系统被应用到不同的扬声器和录音环境没有任何适应。
英文摘要：
Vocal fatigue refers to the feeling of tiredness and weakness of voice due to extended utilization. This paper investigates the effectiveness of neural embeddings for the detection of vocal fatigue. We compare x-vectors, ECAPA-TDNN, and wav2vec 2.0 embeddings on a corpus of academic spoken English. Low-dimensional mappings of the data reveal that neural embeddings capture information about the change in vocal characteristics of a speaker during prolonged voice usage. We show that vocal fatigue can be reliably predicted using all three kinds of neural embeddings after only 50 minutes of continuous speaking when temporal smoothing and normalization are applied to the extracted embeddings. We employ support vector machines for classification and achieve accuracy scores of 81% using x-vectors, 85% using ECAPA-TDNN embeddings, and 82% using wav2vec 2.0 embeddings as input features. We obtain an accuracy score of 76%, when the trained system is applied to a different speaker and recording environment without any adaptation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
