RI: Medium: Collaborative Research: Variance and Invariance in Voice Quality: Implications for Machine and Human Speaker Identification

说话者的声音质量传达了许多种信息，包括单词和话语韵律，情绪状态和个人身份。声源和声道的变化都会影响语音质量，并且可能存在显著的说话者间和说话者内的变化。了解语音的哪些方面是说话者特有的，有助于理解人类在感知说话者差异方面的局限性，并有助于开发更好的说话者识别（SID）算法。尽管技术进步，目前的SID系统的性能仍然远远不够完美，并且当训练和测试条件不匹配时，特别是在语音风格（例如会话与阅读），说话者的情绪状态，当话语很短时，以及当任务是文本独立时，性能显著下降。该项目旨在回答的关键问题是：在正常的日常生活变化下，说话者的声音有多不像他或她自己，而更像其他人？哪些声学特性可以解释说话人的相似性？自动说话人识别（SID）算法是否可以通过了解哪些属性对人类感知说话人相似性很重要来改进？该项目是一个变革性的项目，有助于更好地理解和建模语音质量的变化和不变性。它将告知人类语音感知中的几个重要问题，特别是在说话人相似性方面。了解源信号的哪些方面（如果有的话）是说话者特定的，应该有助于开发更好的说话者识别和验证算法，这些算法能够处理简短的话语，并且对不同的影响和说话风格具有鲁棒性。语音质量变化的模型也可以提高文本到语音（TTS）系统的自然度。如果知道一个人可以在不损害其声音身份的情况下改变他或她的声音质量，这种知识也可以为医疗康复应用和法医学提供信息。因此，更好地了解语音质量将对科学以及工程、法医和医学应用产生重大影响。该项目有强大的推广和传播计划，并促进电气工程，语言学，言语和听力科学在加州大学洛杉矶分校和卓越中心在JHU跨学科活动。它培养本科生和研究生在重要的跨学科活动的技术和科学意义。 研究结果将发表在高质量的期刊上，并在相关的国际会议上发表。研究结果-一套数据库，软件工具和出版物将免费传播。该项目分析并发现在日常生活中引入可变性的情况下，说话者内部和之间的语音信号如何变化。具体来说，它调查是否一个单独的谈话者的语音在录音会话和语音任务显着变化。最重要的是，它研究了如何从所有这些来源的变异性与交谈者之间的变异性比较交谈者内的变异性。理解这些问题需要一个高质量的语音数据库，其中包含来自许多说话者（在本例中为200个）的多个语音样本，这些语音样本被收集、注释并分发给其他研究人员。 声学分析通过生成每个说话者的多维声学简档来揭示不同情况下语音信号中的说话者间和说话者内可变性，所述多维声学简档指定在该说话者的语料库中典型的参数值的范围以及偏离该通常简档的可能性。感知研究确定参数配置文件预测感知相似性的程度，以及在说话者不再像自己说话之前，每个参数的可变性可以容忍多少。从声学和感知研究的见解指导强大的文本相关和文本无关的SID算法的发展，预计将是强大的影响，风格和短话语的变化。

A talker's voice quality conveys many kinds of information, including word and utterance prosody, emotional state, and personal identity. Variations in both the voice source and the vocal tract affect voice quality and there can be significant inter- and intra-talker variability. Understanding what aspects of a voice are talker-specific should aid in understanding the human limits in perceiving speaker differences and in developing better speaker identification (SID) algorithms. Despite technological advances, the performance of current SID systems remains far from perfect, and degrades significantly when the training and testing conditions are mismatched especially in terms of speech style (conversational versus read for example), speaker's emotional status, when the utterances are short, and when the task is text-independent. The key questions that the project aims to answer are: under normal daily life variability, how often does a talker sound less like him- or herself and more like someone else? Which acoustic properties account for speaker similarity? Can automatic speaker identification (SID) algorithms be improved by knowledge of which properties are important for human perception of speaker similarity?The project is a transformative one and helps better understand and model variance and invariance in voice quality. It will inform several important issues in human speech perception, especially in the area of talker similarity. Understanding what aspects of the source signal, if any, are talker-specific, should aid in developing better speaker identification and verification algorithms that are able to handle short utterances and are robust to varying affect and styles of speaking. A model of voice quality variations could also improve the naturalness of text-to-speech (TTS) systems. If it were known how much a person could change his or her voice quality without compromising their vocal identity, this knowledge could also inform medical rehab applications and forensics. A better understanding of voice quality will thus be of significant impact scientifically, and for engineering, forensic, and medical applications. The project has strong outreach and dissemination programs and fosters interdisciplinary activities in Electrical Engineering, Linguistics, and Speech and Hearing Science at UCLA and the Center of Excellence at JHU. It trains undergraduate and graduate students in important cross-disciplinary activities of technological and scientific significance. The results will be published in high-quality journals and presented at relevant international conferences. The research results - a set of databases, software tools, and publications will be disseminated freely.The project analyzes and discovers how the speech signal varies within and across talkers under circumstances that introduce variability in everyday life situations. Specifically, it investigates whether an individual talker's speech varies significantly across recording sessions and speech tasks. Most importantly, it examines how intra-talker variability from all these sources of variability compares with inter-talker variability. Understanding these issues requires a high-quality speech database with multiple voice samples from many talkers (in this case 200) which are collected, annotated, and distributed to other researchers. Acoustic analyses reveals inter- and intra-talker variability in the speech signal across different situations by generating a multi- dimensional acoustic profile of each talker that specifies the range of parameter values that are typical in the corpus for that talker, and the likelihood of deviations from that usual profile. Perceptual studies determine the extent to which parameter profiles predict perceived similarity, and how much variability in each parameter can be tolerated before talkers cease to sound like themselves. Insights from the acoustic and perceptual studies guide the development of robust text-dependent and text-independent SID algorithms that are anticipated to be robust to variations in affect, style, and for short utterances.