EAGER: Collaborative Research: Models of Child Speech

EAGER：合作研究：儿童言语模型

• 批准号: 1551113
• 负责人: Abeer Alwan
• 金额: $ 14万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1551113&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Models; Child

In contrast to the production, modeling, and machine recognition of adult speech, which have been studied for decades, the production, acoustic modeling and recognition of child speech have not received the same level of attention. The lack of scholarly resources for dealing with children's speech is problematic as new applications for child speech and language development become increasingly important and commonplace. This is especially true for elementary school children. As children grow, their articulators grow as well, resulting in variations in their speech sounds. For example, the waveform of the word 'sunny' spoken by a 6-year old can be quite different than that of the same child when she is 9 years old. This is why current machine recognition of children's speech does not perform well for young children and does not scale up as the child grows. That is, these systems tend to be age dependent. Understanding and modeling child speech as children grow is important not only to developing better recognition systems but also for better understanding and diagnosis of speech-language pathology (SLP). As the negative long-term ramifications of deficits in early childhood development gain increasingly broad recognition, the opportunities and the need for social and health services and technological applications targeted toward young children are growing. In particular, it is now understood that early deficits in language development and literacy persist into adulthood, and the demand for SLP services in public schools is significantly outpacing supply. As a result, it is no longer feasible for clinicians and teachers to provide the most effective treatments or the necessary attention to every child. Better speech recognition systems would provide an opportunity for improved diagnosis and more intense computer-based therapy. This Early Grant for Exploratory Research project aims to model how speech and language develop during elementary school and how children with speech disorders differ in their articulation of speech sounds. Models of child speech will lead to the development of computer programs which can be used for educational as well as therapeutic purposes.Scientifically, the exploratory project will 1) reveal processes of speech production development in 20-26 elementary school-aged children through a unique combination of articulatory and acoustic analyses, and 2) develop acoustic models and eventually automatic speech recognition systems for children's speech which can be scalable with age (as opposed to being age-dependent systems). This can only be achieved by understanding how the articulation and corresponding acoustics develop with age. The different aspects of the project are therefore synergistic: findings from articulation and acoustic experiments will inform the development of algorithms essential to automatic speech recognition. Production data will include real-time 3D ultrasound recordings of the tongue, video recordings of the lips, palate impressions, microphone recordings, and accelerometer recordings of neck skin vibrations which have been shown to be beneficial in automatic speech and speaker recognition applications. The causal relationship between articulatory and acoustic variability will be explored, as will their relationship to misrecognition of child speech. Articulatory features will be incorporated into new automatic speech recognition systems along with acoustic features. The exploratory project will contribute to knowledge of variability between children, as well as variability over time as children grow and will provide, for the first time, normative data and scientific models. These can lead to robust child speech recognition systems as well as tools that will be useful for a variety of applications such as educational games, training of speech-language pathologists, automatic or semi-automatic transcription systems, and speech articulation visualization systems. It will train undergraduate and graduate students in important cross-disciplinary activities of technological and scientific significance. We believe that the proposed project is transformative in its advancement of the scientific and technological state of the art related to child speech.

与成人语音的产生、建模和机器识别（已经研究了几十年）相比，儿童语音的产生、声学建模和识别并没有受到同等程度的关注。缺乏学术资源来处理儿童的讲话是有问题的，因为新的应用程序，儿童的讲话和语言发展变得越来越重要和普遍。这对小学生来说尤其如此。随着孩子的成长，他们的发音器官也在成长，这导致了他们说话声音的变化。例如，一个6岁的孩子说出“sunny”这个词的波形可能与同一个孩子9岁时的波形完全不同。这就是为什么目前的儿童语音机器识别对幼儿的表现不佳，并且不会随着儿童的成长而扩大。也就是说，这些系统往往是年龄依赖性的。随着儿童的成长，理解和建模儿童语音不仅对开发更好的识别系统很重要，而且对更好地理解和诊断语音语言病理学（SLP）也很重要。随着幼儿发展缺陷的长期负面影响得到越来越广泛的认识，针对幼儿的社会和保健服务以及技术应用的机会和需求也在增加。特别是，现在人们知道，语言发展和识字的早期缺陷持续到成年，公立学校对SLP服务的需求远远超过供应。因此，临床医生和教师不再能够为每个儿童提供最有效的治疗或必要的关注。更好的语音识别系统将为改善诊断和更密集的计算机治疗提供机会。 这个探索性研究项目的早期补助金旨在模拟小学期间言语和语言的发展以及言语障碍儿童在语音清晰度方面的差异。 儿童语言模型将导致计算机程序的开发，可用于教育以及治疗目的。科学上，探索性项目将1）通过发音和声学分析的独特组合揭示20-26岁小学儿童的语言产生发展过程，以及2）开发声学模型，并最终开发用于儿童语音的自动语音识别系统，该系统可以随年龄而扩展（与依赖于年龄的系统相反）。这只能通过了解发音和相应的声学如何随着年龄的增长而发展来实现。因此，该项目的不同方面是协同的：发音和声学实验的结果将为自动语音识别所必需的算法的开发提供信息。 生产数据将包括舌头的实时3D超声记录，嘴唇的视频记录，腭印象，麦克风记录和颈部皮肤振动的加速度计记录，这些记录已被证明在自动语音和说话人识别应用中是有益的。发音和声学变异之间的因果关系将被探讨，因为他们的关系，以儿童语音识别错误。发音特征将与声学特征一起沿着被纳入新的自动语音识别系统。该探索性项目将有助于了解儿童之间的差异以及随着儿童成长而发生的差异，并将首次提供规范数据和科学模型。这些可以导致强大的儿童语音识别系统以及工具，将是有用的各种应用，如教育游戏，语音语言病理学家的培训，自动或半自动转录系统，语音清晰度可视化系统。它将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。我们相信，拟议的项目是变革性的，在其先进的科学和技术的国家有关的儿童语言。