A computational model of target oriented production of prosody

面向目标的韵律生成的计算模型

• 批准号: 5327916
• 负责人: Professor Dr. Grzegorz Dogil (†)
• 金额: --
• 依托单位: Institut für Maschinelle Sprachverarbeitung (IMS)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2001
• 资助国家: 德国
• 起止时间: 2000-12-31 至 2003-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/5327916?language=en
• 关键词: computational; model; target; oriented; production

The two main goals of the proposed project are, first, to establish a novel paradigm of research into the production of prosody and, second, to provide experimental evidence for several assumptions made by the computational model underlying the proposed approach. In the framework of the proposed research project a computational prosody model will be implemented that has its motivation both in the theory of speech production and in linguistic theory. The computational model is intended to serve two main purposes. First, it will allow us to empirically test a number of assumptions made by the production model, for instance the effect of speaking rate and other factors related to speech timing on the acoustic realization of intonational gestures. Second, the linguistically based classification of intonational events, e g those related to (a) discourse structure (register, pitch range), (b) information structure (topic, focus), and (c) accentual patterns (pitch accents, tones, tunes), can be experimentally tested by trainable intonation event classifiers. In accordance with the postural relaxation hypothesis we expect such a learned neural mapping, based on adjustable adaptive weights, to tend consistently towards comfortable realization configurations, either under the influence of temporal constraints or as part of a tune (a coherent sequence of intonational events), as long as the perceptual target region is traversed.

拟议项目的两个主要目标首先是建立对韵律生产的研究范式，其次是为提出方法基础的计算模型提供的几种假设提供实验证据。在拟议的研究项目的框架内，将实施计算韵律模型，该模型在语音生产理论和语言理论中都具有动力。计算模型旨在实现两个主要目的。首先，这将使我们能够经验测试生产模型做出的许多假设，例如，语言速率和与语音时机有关的其他因素的影响对语言手势的声学实现。其次，基于语言事件的基于语言的分类，与（a）与（a）话语结构（寄存器，音调范围）相关的事件的分类，（b）信息结构（主题，焦点）和（c）强调模式（音调重音，音调，调音，调音） ），可以通过可训练的语调事件分类器对实验测试。根据姿势放松假说，我们希望基于可调的适应权重的这种学识渊博的神经映射倾向于在时间约束的影响下或作为音调的一部分（连贯的int式事件序列）始终朝着舒适的实现配置倾向，只要经过感知目标区域。