Person-specific automatic speaker recognition: understanding the behaviour of individual speakers for applications of ASR

自动说话人识别(ASR)软件对语音进行处理和分析，以决定两个语音属于同一个人还是不同的人。这类技术正在成为我们生活中日益重要的一部分；在访问个人帐户(例如银行)时用作安全措施，或用作在智能设备上针对特定人定制内容的手段。在世界各地，ASR系统通常用于调查和法医目的，分析身份不明的犯罪声音的录音。然而，对于某些声音，系统表现得更好或更差。因此，一个根本的问题仍然存在：是什么让ASR识别特定的声音容易或困难？使用人工智能(AI)技术的最先进系统在性能上与旧方法相比有了显著的提高。然而，仍然存在一些问题。首先，ASR研究的重点是将众所周知的技术因素的影响降至最低，如渠道(例如移动电话与固定电话)、录音质量和麦克风。在解决这些技术挑战的过程中，在系统方面取得了很大的改进。然而，人们对说话者本身是如何影响ASR性能的知之甚少。其次，ASR研究一直对降低总体错误率感兴趣。然而，在现实世界中(无辜和有罪可能岌岌可危)，关键问题是：在这种特定情况下，系统出错的可能性有多大？最后，尽管人工智能方法无疑带来了整体性能的改善，但此类算法使人们更难知道信息系统依赖于什么来做出决策。这对法医专家来说是有问题的，他们必须向非专家最终用户，如法官、陪审团、律师和警察解释他们的方法。这个项目是第一个系统地评估单个说话者在ASR系统内和整个ASR系统中的表现，并比较说话人的影响，根据声音的语言属性或说话人的人口统计(例如口音、种族、性别)，与经过充分研究的技术影响。其目的是利用这一知识通过标记潜在的有问题的说话者来改进ASR系统，并开发处理这些有问题的说话者的方法。我们将使用新的、跨学科的方法，汇集语音技术、语言学和法医语音科学的专业知识。我们与商业ASR供应商Oxford Wave Research的合作使我们能够调整和更改系统，以评估对个别演讲者结果的影响。我们还将使用高度受控的小规模实验来单独评估扬声器的效果，并使用由我们的项目合作伙伴英国国防部和荷兰法医研究所提供的更具取证现实意义的录音的更大数据集。各种数据集的可获得性也使我们能够评估各种声音结果的概括性。这个项目完全是由现实世界的问题驱动的，因此其结果将对广泛的利益攸关方产生相当大的影响。通过更多地了解个体，我们的结果有能力提高整体ASR性能。这将有利于ASR系统的用户和开发人员。结果还将对法医和调查应用产生具体影响，指导数据收集以验证方法(专家们面临着越来越大的监管压力)，并为ASR和语言分析相结合提供了一个框架。在这样做的过程中，通过与法律界的接触，我们的目标是影响ASR在英格兰和威尔士的地位的改变，以便它可以被接受为专家证据。我们将通过与法医咨询小组进行知识交流来产生影响，该小组由来自法医言语科学、执法部门和法律界的代表组成。

Automatic speaker recognition (ASR) software processes and analyses speech to make decisions about whether two voices belong to the same or different individuals. Such technology is becoming an increasingly important part of our lives; used as a security measure when gaining access to personal accounts (e.g. banks), or as a means of tailoring content to a specific person on smart devices. Around the world, ASR systems are commonly used for investigative and forensic purposes, to analyse recordings of criminal voices where identity is unknown. Yet systems perform better or worse with certain voices. Therefore, a fundamental question remains: what makes a particular voice easy or difficult for ASR to recognise?State-of-the-art systems, using techniques from artificial intelligence (AI), have shown marked improvements in performance compared with older approaches. However, there remain issues. Firstly, ASR research has focused on minimising the effects of well-known technical factors, such as channel (e.g. mobile vs. landline telephone), recording quality and microphones. In resolving these technical challenges, large improvements in systems have been achieved. Yet little is known about how speakers themselves affect ASR performance. Secondly, ASR research has been interested in reducing overall error rates. Yet, in the real-world (where innocence and guilt may be at stake), the key question is: what is the chance the system has made an error in this specific instance? Finally, while AI approaches have undoubtedly brought improvements in overall performance, such algorithms make it more difficult to know what information systems are relying on to make decisions. This is problematic for forensic experts, who must explain their methods to non-expert end users, such as judges, juries, lawyers and police.This project is the first to systematically assess how individual speakers perform within and across ASR systems and to compare speaker effects, in terms of linguistic properties of voices or speaker demographics (e.g. accent, ethnicity, gender), with well-studied technical effects. The aim is to use this knowledge to improve ASR systems by flagging potentially problematic speakers and to develop methods to handle these problematic speakers. We will use novel, interdisciplinary methods, bringing together expertise from speech technology, linguistics, and forensic speech science. Our collaboration with commercial ASR vendor Oxford Wave Research allows us to adapt and change systems to assess the effects on results for individual speakers. We will also use highly controlled, small-scale experiments to assess speaker effects in isolation, as well as using much larger datasets of more forensically realistic recordings, provided by our project partners, the UK Ministry of Defence and the Netherlands Forensic Institute. The availability of a variety of datasets also allows us to assess the generalisability of results across a range of voices. This project is entirely driven by real-world issues and so the results will deliver considerable impact to a wide range of stakeholders. By understanding more about individuals, our results have the capability to improve overall ASR performance. This will be of benefit to users and developers of ASR systems. The results will also have specific implications for forensic and investigative applications, guiding data collection for validating methods (something which experts are under increasing regulatory pressure to do) and provide a framework for combining ASR and linguistic analysis. In doing so, through engagement with the legal community, we aim to affect a change in the status of ASR in England and Wales, such that it is admissible as expert evidence. We will deliver impact via knowledge exchange with a Forensic Advisory Panel consisting of representatives from forensic speech science, law enforcement, and the legal community.