Collaborative Research: Effects of production variability on the acoustic consequences of coordinated articulatory gestures

人类的声音，我们最古老和最可靠的通信工具，现在正迅速成为我们每天使用的输入界面，与汽车导航系统，医疗和法律的听写系统，个人助理如“Siri”，自动金融系统等技术进行交互。 语音识别是所有这些技术的支柱。 因此，语音识别器的性能是客户满意度的关键。 目前，许多系统仍然需要针对特定说话者进行调整以表现良好，并且识别任务必须以其他方式受到限制，例如要求（1）使用特定词汇，（2）大多数单词（尤其是内容单词）的清晰发音，以及（3）有限的背景噪声。在这项研究中，语音变异性将被研究，方法和模型将被开发，这将使识别器更加扬声器独立，并能够处理从清晰的发音到非常随意的语音的全方位的语音风格。结果也将承担语言模型的语音规划和组织，提供证据的扬声器如何权衡效率在生产的语音对需要是可理解的。在这个项目中，点源跟踪的语音发音将同时收集相应的声学。 说话者将以正常和快速的速度记录语音（后者的目的是显着增加信号的可变性程度）。 该数据将允许调查说话者是否总是在期望目标的方向上移动他们的语音发音器（例如，在产生/t/时从舌尖到牙齿），即使当快速的产生节奏阻塞相关声学信息时（如在“完美”中）。 如果得到证实，这一发现将为通过纳入发音信息使识别系统更加强大指明方向。 此外，这些数据将支持语音反转系统的开发，该系统能够“发现”隐藏的发音运动，这些运动可能被声学掩盖。

The human voice, our oldest and most reliable communication tool, is now rapidly becoming the input interface of choice that we use everyday to interact with technologies such as car navigation systems, medical and legal dictation systems, personal assistants like "Siri," automated financial systems, etc. Thousands of 'apps' have been developed to help consumers use voice to get the information they are looking for. Speech recognition is the backbone of all of these technologies. As a result, the performance of speech recognizers is key for customer satisfaction. Currently, many systems still need to be tuned for a particular speaker to perform well, and the recognition task has to be limited in other ways such as requiring (1) usage of a specific vocabulary, (2) clear pronunciation of most of the words, especially the content words and (3) limited background noise. In this research, speech variability will be studied, and methods and models will be developed that will enable recognizers to be more speaker independent and capable of handling the full range of speech styles from clear articulation to very casually spoken speech. The results will also bear on linguistic models of speech planning and organization, providing evidence for how speakers trade off efficiencies in the production of speech against the need to be intelligible.In this project, point source tracking of the speech articulators will be collected concurrently with the corresponding acoustics. Speakers will record speech at both a normal and rapid pace (the purpose of the latter is to increase significantly the degree of variability in the signal). This data will allow for the investigation of whether speakers always move their speech articulators in the direction of a desired target (e.g. tongue tip to teeth in producing /t/) even when a rapid production pace occludes the relevant acoustic information (as in "perfect"). If confirmed, this finding will point the way towards making recognition systems more robust through the incorporation of articulatory information. In addition, such data will support the development of a speech inversion system capable of 'uncovering' hidden articulatory movements potentially masked from the acoustics.