工作场所的环境健康是员工身心健康的基本保证,其中声环境对员工情绪健康和工作绩效影响显著.基于开放式办公空间的广泛应用和空间开放性带来的特殊声学问题,近10年间,学界持续而深入地展开研究,取得了丰富的成果.以Web of ScienceTM核...工作场所的环境健康是员工身心健康的基本保证,其中声环境对员工情绪健康和工作绩效影响显著.基于开放式办公空间的广泛应用和空间开放性带来的特殊声学问题,近10年间,学界持续而深入地展开研究,取得了丰富的成果.以Web of ScienceTM核心合集为数据来源,利用CiteSpace可视化图谱分析工具对国内外的环境与建筑声学、心理声学、职业健康、环境心理学、工效学、人因技术等领域的相关研究成果进行文献分析,归纳研究热点,总结研究发现,综述开放式办公空间的声环境问题及其对办公人员的健康影响.研究发现,开放式办公空间的主要干扰声源是作为背景声存在的无关言语;言语私密性是开放式办公空间声环境最主要的问题;语言传输指数STI(speech transmission index)能够很好地表征空间的言语私密性, STI越高,无关言语的可懂度越高,产生的干扰就越大.声环境问题产生的健康影响可以概括为心理干扰和认知干扰两个层面.前者引发声烦恼,加强疲劳、压力、烦躁等负面情绪;后者降低员工的认知绩效,增加认知负荷.为保证开放式办公空间声环境的健康和舒适,应在理论研究、技术探究和管理政策等层面深入推进,尽快建立适应我国办公场所和汉语言环境特征的评价标准,作为职业健康管理、办公建筑设计和声环境改造的依据.展开更多
Speech is easily leaked imperceptibly.When people use their phones,the personal voice assistant is constantly listening and waiting to be activated.Private content in speech may be maliciously extracted through automa...Speech is easily leaked imperceptibly.When people use their phones,the personal voice assistant is constantly listening and waiting to be activated.Private content in speech may be maliciously extracted through automatic speech recognition(ASR)technology by some applications on phone devices.To guarantee that the recognized speech content is accurate,speech enhancement technology is used to denoise the input speech.Speech enhancement technology has developed rapidly along with deep neural networks(DNNs),but adversarial examples can cause DNNs to fail.Considering that the vulnerability of DNN can be used to protect the privacy in speech.In this work,we propose an adversarial method to degrade speech enhancement systems,which can prevent the malicious extraction of private information in speech.Experimental results show that the generated enhanced adversarial examples can be removed most content of the target speech or replaced with target speech content by speech enhancement.The word error rate(WER)between the enhanced original example and enhanced adversarial example recognition result can reach 89.0%.WER of target attack between enhanced adversarial example and target example is low at 33.75%.The adversarial perturbation in the adversarial example can bring much more change than itself.The rate of difference between two enhanced examples and adversarial perturbation can reach more than 1.4430.Meanwhile,the transferability between different speech enhancement models is also investigated.The low transferability of the method can be used to ensure the content in the adversarial example is not damaged,the useful information can be extracted by the friendly ASR.This work can prevent the malicious extraction of speech.展开更多
Speech data publishing breaches users'data privacy,thereby causing more privacy disclosure.Existing work sanitizes content,voice,and voiceprint of speech data without considering the consistence among these three ...Speech data publishing breaches users'data privacy,thereby causing more privacy disclosure.Existing work sanitizes content,voice,and voiceprint of speech data without considering the consistence among these three features,and thus is susceptible to inference attacks.To address the problem,we design a privacy-preserving protocol for speech data publishing(P3S2)that takes the corrections among the three factors into consideration.To concrete,we first propose a three-dimensional sanitization that uses feature learning to capture characteristics in each dimension,and then sanitize speech data using the learned features.As a result,the correlations among the three dimensions of the sanitized speech data are guaranteed.Furthermore,the(ε,δ)-differential privacy is used to theoretically prove both the data privacy preservation and the data utility guarantee of P3S2,filling the gap of algorithm design and performance evaluation.Finally,simulations on two real world datasets have demonstrated both the data privacy preservation and the data utility guarantee.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant No.61300055)Zhejiang Natural Science Foundation(Grant No.LY20F020010)+2 种基金Ningbo Science and Technology Innovation Project(Grant No.2022Z075)Ningbo Natural Science Foundation(Grant No.202003N4089)K.C.Wong Magna Fund in Ningbo University.
文摘Speech is easily leaked imperceptibly.When people use their phones,the personal voice assistant is constantly listening and waiting to be activated.Private content in speech may be maliciously extracted through automatic speech recognition(ASR)technology by some applications on phone devices.To guarantee that the recognized speech content is accurate,speech enhancement technology is used to denoise the input speech.Speech enhancement technology has developed rapidly along with deep neural networks(DNNs),but adversarial examples can cause DNNs to fail.Considering that the vulnerability of DNN can be used to protect the privacy in speech.In this work,we propose an adversarial method to degrade speech enhancement systems,which can prevent the malicious extraction of private information in speech.Experimental results show that the generated enhanced adversarial examples can be removed most content of the target speech or replaced with target speech content by speech enhancement.The word error rate(WER)between the enhanced original example and enhanced adversarial example recognition result can reach 89.0%.WER of target attack between enhanced adversarial example and target example is low at 33.75%.The adversarial perturbation in the adversarial example can bring much more change than itself.The rate of difference between two enhanced examples and adversarial perturbation can reach more than 1.4430.Meanwhile,the transferability between different speech enhancement models is also investigated.The low transferability of the method can be used to ensure the content in the adversarial example is not damaged,the useful information can be extracted by the friendly ASR.This work can prevent the malicious extraction of speech.
基金National Natural Science Foundation of China(No.61902060)Shanghai Sailing Program,China(No.19YF1402100)Fundamental Research Funds for the Central Universities,China(No.2232019D3-51)。
文摘Speech data publishing breaches users'data privacy,thereby causing more privacy disclosure.Existing work sanitizes content,voice,and voiceprint of speech data without considering the consistence among these three features,and thus is susceptible to inference attacks.To address the problem,we design a privacy-preserving protocol for speech data publishing(P3S2)that takes the corrections among the three factors into consideration.To concrete,we first propose a three-dimensional sanitization that uses feature learning to capture characteristics in each dimension,and then sanitize speech data using the learned features.As a result,the correlations among the three dimensions of the sanitized speech data are guaranteed.Furthermore,the(ε,δ)-differential privacy is used to theoretically prove both the data privacy preservation and the data utility guarantee of P3S2,filling the gap of algorithm design and performance evaluation.Finally,simulations on two real world datasets have demonstrated both the data privacy preservation and the data utility guarantee.
