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【期刊名称】 《刑事技术》
2017年司法语音及声学研究
【英文标题】 A Review on Researches of Forensic Phonetics and Acoustics in 2017
【作者】 康锦涛王莉王晓笛盛卉李敬阳黄文林
【作者单位】 公安部物证鉴定中心,2011计划司法文明协同创新中心公安部物证鉴定中心,2011计划司法文明协同创新中心公安部物证鉴定中心,2011计划司法文明协同创新中心公安部物证鉴定中心,2011计划司法文明协同创新中心
【分类】 刑事侦察学
【中文关键词】 司法语音及声学;语音同一认定;语音人身分析;录音的真实性检验;降噪及语音增强
【英文关键词】 forensic phonetics and acoustics; forensic speaker identification; speaker profiling; audio authentication; noise reduction and speech enhancement
【文章编码】 1008-3650(2018)03-0179-08
【文献标识码】 A DOI: 10.16467/j.1008-3650.2018.03.002
【期刊年份】 2018年【期号】 3
【摘要】 司法语音及声学在2017年发展迅速,本文对其中的代表性成果进行梳理。在语音同一认定方面,VPA成为听觉分析的热点,一些机构对鉴定意见表述分类更加细致,自动识别技术不断发展,其与专家鉴定的关系各方仍有不同意见,部分机构与学者开始推动检验过程向更加透明化的方向发展。在语音人身分析方面,除了传统的对性别、年龄、地域、体形等属性的推断,语音情感分析也成为研究热点,无论是专家分析还是自动分析都取得了进步。录音的真实性检验中,除了传统检验流程的深入研究,电网频率仍是关注热点。降噪及语音增强开始使用深度神经网络技术并取得了一定成果。
【英文摘要】 Forensic phonetics and acoustics have made fast development in 2017. This paper gives a short introduction to their state-of-the-art techniques. In forensic speaker identification (FSI), many scholars have made efforts in standardizing the procedures of auditory analysis. These procedures are being used in several forensic speaker comparison laboratories around the world. Besides, Vocal profile analysis (VPA) is the focus of the techniques and several scholars have devised different versions of VPA for forensic purpose, and its utility even extends to the optimization of automatic speaker comparison systems. Phonetic-acoustic analysis has been broadened in different directions, including filled pauses, diphthongs and voice profiling. The value of diverse vocal characteristics is measured in different ways; concepts like relevant population and homogeneity measurement are initiated to quantize accurately. Evaluation system is divided into more scales in some institutions for various reasons like avoiding cliff-edge effect and keeping accordance with other forensic counterparts. Automatic speaker recognition moves forward although there are controversies over its relationship with experts and its role in FSI. Whether conclusions from these machines should be accepted in court as evidence is argued around the world and yielding different attitudes. Some institutions and professionals are working toward building a more transparent environment for FSI. In speaker profiling, apart from traditional characteristics like age, sex, body size and region, both emotion and deception detection are becoming more and more intelligence-intensive in human and automatic ways; several I-vector based systems are designed to infer speakers’ information from voices automatically, getting good results. In audio authentication, besides further developments in traditional examination procedures and methods, electric network frequency and its forensic application are still elaborated in many sub-disciplines, such as the construction of ENF database and automatic comparison methods. In noise reduction and speech enhancement, an integration is being made with new techniques such as deep neural network. Different algorithms and systems have been desvised in cancelling reverberation, enhancing speech while reducing noises.
【全文】法宝引证码CLI.A.1239700    
  
  司法语音及声学在我国即为广义上的声纹鉴定,包括司法语音学检验中的语音同一认定、语音人身分析、语音内容辨识和司法声学检验中的录音的真实性检验、降噪及语音增强、噪声分析、音源同一鉴定以及录音器材鉴定等内容{1}。国外司法语音及声学的研究内容与我国大致相同{2}。2017年,语音同一认定仍是司法语音及声学的主要内容,其在听觉分析、语音学-声学分析、自动识别、质量控制等方面均产生了新的成果;语音人身分析除传统的性别、年龄等特征外,语音情感分析也成为重要内容,并在自动识别方面发展迅速;各国学者也在录音的真实性检验以及降噪及语音增强等方向做了开拓。本文对2017年司法语音及声学领域的语音同一认定、语音人身分析、录音的真实性检验、降噪及语音增强等热点专业的代表性成果进行介绍。
  1 语音同一认定
  语音同一认定在我国即为狭义上的声纹鉴定{3},它也是司法语音及声学实践中的主要分支{4}。目前,国际上的语音同一认定实践中,绝大多数机构与从业者采用的是听觉分析与声学分析相结合的专家鉴定方法{5},但也有一些机构开始尝试将自动识别的方法引入语音同一认定领域,采用半自动(专家干预)或自动识别等方法开展实践{6-7}。2017年,关于语音同一认定的专业论述多数集中在听觉分析方法、语音学及声学特征分析、语音特征的鉴定价值、鉴定意见表述、自动识别技术以及语音同一认定过程中的质量控制与标准化等方面。
  1.1 听觉分析
  听觉分析是目前语音同一认定技术方法的重要组成部分[1, 8-10],在国内外许多规范标准中早有明确规定{11-16}。2017年,Sundqvist等{17}设计了一套听觉分析程序,并将之应用于瑞典国家法庭科学中心(NFC)的检验实践中。为了推进听觉分析方法的体系化与规范化,Lindh等{18}对听觉分析方法的可靠性做了考察,分别使用听觉分析与自动识别对芬兰语说话人进行对比分析,并用于芬兰国家调查局(NBI)的语音同一认定实践的流程改进。Leinonen等{19}提出建立不同语种的听觉特征集,并在瑞典语和芬兰语两个语种上开始了初步尝试。Land等{20}对笑声的听觉分析价值进行了探讨。在伪装语音的研究方面,Skarnitzl与R??i?ková等{21-22}研究了捷克语说话人的常见伪装方式,并对不同伪造方式下的听觉特征与声学特征做了初步分析,Delvaux等{23}考察了伪装与模仿两种方式下听觉特征与声学特征的差异。
  嗓音特质分析(Vocal Profile Analysis, VPA)在语音同一认定中的应用是近年来听觉分析研究的热点{24-29},2017年,许多专家学者继续就这一方向进行探索。为了便于分析,Segundo等{30}设计了简化的VPA分析表,并应用于同卵双胞胎的听觉分析上;Segundo等{31}验证了VPA分析表在西班牙语、德语、英语语境下的有效性。Klug{32}就VPA分析表的改进做了探讨,提出应当在加强培训的基础上改进要素的类目。Hughes等{33-34}将VPA分析表得分与自动识别方法结合起来考察,结果表明,将使用梅尔频率倒谱系数(MFCC)参数与长时共振峰分布(LTFD)特征的自动识别系统融合,系统性能提升有限,将VPA得分结果加入后,系统识别正确率显著增加。
  1.2 语音学-声学分析
  听觉分析与语音学-声学分析是共生互补的关系{35-36},语音学-声学分析方法不仅为听觉分析提供量化支持,而且也可以提供新的特征{3}。在语音学声学分析方面,Heuven、Gold等{37-38}继续就填词暂停(filled pauses)、犹豫词(hesitation markers)的声学特征进行分析,以进一步挖掘其在语音同一认定中的价值。He等{39}研究了不同说话人的重音变化受噪音或不同频段影响的程度,结果表明不同说话人的重音特征在全频段上都有较好的体现。双语者在说两种语言时的声学特征各有何特点是一直以来的研究课题之一,Dorreen等{40}就这个课题下的长时基频分布做了研究。Arantes等{41}考察了语种、话语方式等因素对长时基频达到稳定状态时的时长影响,结果表明话语方式的影响最大。Dimos、Lopez等{42-43}研究了大喊状态下语音的节奏、韵律以及频谱特征。He等{44}研究了音强曲线的声纹鉴定价值。不同语种的元音空间(vowel space)并不相同,Varo?anec-?kari?{45}研究了克罗地亚语、塞尔维亚语和斯洛文尼亚语男性说话人元音空间的异同,为开展不同语种间的说话人鉴定提供了一定基础。McDougall等{46}比较了基于音节与基于时间的两种流利度描写方法。Wang等{47}研究了汉语复合元音的动态特征,结果表明复合元音也具备较高的声纹鉴定价值。Heeren{48}对电话录音中[s]在不同语境下的不同声学特性进行了探讨。在嗓音档案(voice profile)的构建方面,Franchini{49}以[l]音的声学特征为例对此做了研究,Fingerling{50}对二语说话人的元音集合重建做了探索。
  1.3 语音特征的价值
  在语音同一认定中,语音特征价值的高低是需要重点考虑的内容。根据语音特征的动态性原理,其具有变异性(即同一说话人的自身的差异)和差异性(即不同说话人之间的差异),变异小而差异大的特征鉴定价值较高。2017年,对于特征价值的关注点主要在人群的语音特征分布上。Rhodes等{51}认为现阶段的人群特征分布研究应与实际案件结合。Hughes、Wormald{52}提出建立维基方言库的构想,将方言中的高价值特征放入数据库。Hughes等{53}提出了研究人群语音特征分布需要考虑的四个问题,一是控制因子,二是特异度,三是误差,四是确定程度,并以英语中双元音[ai]中的共振峰走势为例,说明了不同情况下的语音特征分布对语音同一认定结果的可能影响。在检材与样本内部语音特征的表现是否稳定方面,在以往部分研究的基础上,Ajili{54-56}提出一种使用信息论中的同质化度量(homogeneity measure)标准对声学参数的稳定性进行度量的方法{57}。
  1.4 声纹鉴定意见表述
  声纹鉴定的意见表述一直以来都是讨论热点。国际上,Rose和Morrison一直提倡量化的似然比体系,英国的Nolan等绝大部分从业人员使用英国立场声明形式,欧洲大陆的大部分从业者则使用可能性等级形式。我国则多使用5级分类的可能性等级形式{11}。
  2017年,英国的French{58}调整了其意见表述形式,逐渐从英国立场说明框架下的一致性与独特性{59}转向可能性等级形式,在这一框架下,意见共分为13级,与英国法庭科学提供者协会(Association of Forensic Science Providers)推荐的标准{60}一致。荷兰NFI的Vermeulen{61}介绍了其得出“强烈支持”结论的依据,在实际案例中,NFI只有在检材与样本特征几乎相同或者说话人有言语障碍等高度独特性特征时才给出这种鉴定意见。
  1.5 语音数据库及自动识别技术
  目前,国际上司法语音及声学专门的语音数据库有英国的Nolan建立的DyVis{62}、澳大利亚的Morrison建立的FVCD{63}、西班牙的Ramos建立的AHUMADA{64}、荷兰的Vloed建立的NFI-FRITS{65}、法国的Ajili建立的FABIOLE{66}等。国内方面,我国的“全国公安机关声纹数据库”依然是国际上收录说话人最多的声纹鉴定语音数据库。2017年新建的VoxCeleb{67}则是比较新的代表。目前说话人自动识别技术的主流框架主要有两类,一种是高斯混合模型加通用背景模型(GMM-UBM),另一种是基于i向量(i-vector)空间的概率线性判别分析(PLDA)方法,同时开始使用深度神经网络(deep neural network, DNN)提取语音特征。后一种框架较新,因此成为2017年的研究热点。DNN提取语音特征的方法取得的效果较好,对训练数据量的要求也较大,我国的“全国公安机关声纹数据库”已经采用DNN方法提取特征。Park等{68}将嗓音音质声学特征引入采用这种架构的自动识别系统中,与MFCC特征结合,显著提升了短语音的识别率。Solewicz等{69}为解决现有的对数似然比(LLR)对处理说话人内部变异的不足提出了一种新的说话人自动识别系

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【注释】                                                                                                     
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