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.

该项目的两个主要目标是，第一，建立一种新的韵律产生研究范式；第二，为支持该方法的计算模型所作的几个假设提供实验证据。在拟议的研究项目框架内，将实施一个计算韵律模式，该模式在言语产生理论和语言学理论中都有其动机。计算模型的目的主要有两个。首先，它将使我们能够实证地测试生产模型所做的一些假设，例如语速和其他与语音计时相关的因素对语调手势的声学实现的影响。其次，基于语言学的语调事件分类，例如与(A)语篇结构(语域、音调范围)、(B)信息结构(话题、焦点)和(C)重音模式(音调、声调、调子)相关的分类，可以由可训练的语调事件分类器进行实验测试。根据姿势松弛假说，我们期望这种基于可调自适应权重的学习神经映射，无论是在时间限制的影响下还是作为音调(音调事件的连贯序列)的一部分，只要感知目标区域被遍历，就会一致地趋向于舒适的实现配置。