RI: Small: Emotional Speech Production: Analysis, Modeling and Synthesis

言语中的情感编码是通过声音调制来实现的，这需要对人类发声和声道清晰度进行复杂的控制。本研究的目的是基于先进的语音生产数据采集技术，包括电磁关节成像（EMA）和实时磁共振成像（rt-MRI），识别和建模情感语音生产的发音过程。该研究的重点是直接测量和建模发音运动学及其相互作用与韵律调制的音高，响度和分段持续时间在言语情感表达，以了解情绪言语生产策略跨情感类型以及跨扬声器。情感语音产生模型的有效性进行了验证，通过使用软件发音合成器在分析合成的方式。这些发现的理论意义被解释为与超和次理论以及语音产生的转换器/分配器（C/D）模型有关。关于情感对人类语音发音和韵律模式的影响的详细知识在开发用于情感语音识别和合成的改进的语音处理技术方面具有变革性的潜力，这些语音识别和合成对于自然和健壮的人类的发展至关重要。机器接口。这一目标还包括为表达性言语的定量评估提供信息，以表征不同人群中的非典型或痛苦的声音行为，例如自闭症谱系障碍（ASD）儿童。最后，这项研究工作的一个自然副产品是独特的发音数据库，该数据库将与社区免费共享，以进一步扩展人类语音产生的知识。

Encoding of emotions in speech is achieved by vocal modulations that require an intricate control of human voicing and vocal tract articulation. The aim of this research is to identify and model articulatory processes for emotional speech production based on advanced speech production data acquisition technologies including Electromagnetic articulography (EMA) and a real-time Magnetic resonance imaging (rt-MRI). The research focuses on directly measuring and modeling articulatory kinematics and their interplay with prosodic modulation of pitch, loudness and segmental durations in speech emotion expression in order to understand the emotional speech production strategies across emotion types as well as across speakers. The validity of the emotional speech production models is verified by using a software articulatory synthesizer in an analysis-by-synthesis fashion. Theoretical implications of the findings are interpreted in relation to the Hyper and Hypo theory and the Converter/Distributor (C/D) model of speech production.Detailed knowledge on the effects of emotion on the human speech articulatory and prosodic patterning has transformative potential in developing improved speech processing technologies for emotional speech recognition and synthesis that are critical for the development of natural and robust man-machine interfaces. This goal also critically includes informing quantitative assessment of expressive speech to characterize atypical or distressed vocal behavior in diverse populations, for instance, children with Autism Spectrum Disorder (ASD). Finally, a natural by-product of this research effort is the unique articulatory database that will be shared freely with the community for further expansion of the knowledge of human speech production.