RI: Statistical Modeling of Prosodic Features in Speech Technology

这个项目的目标是开发一个建模框架，以便能够在需要口语理解的大类应用中广泛使用韵律信息，如音调、持续时间和能量特征。为此，从由可自动检测的事件定义的区域上的语音信号中提取韵律特征。结果是一个通常是高维向量的可变长度序列，具有混合的离散和连续分布以及未定义的值。该项目的重点是寻找一种变换，当应用于韵律特征时，产生能够充分代表原始韵律特征序列的重要特征的单个向量。该变换通过将样本中特征的分布投影到由动态贝叶斯网络表示的一组概率分布上形成，最终目标是创建一种通用的基于概率模型的变换范例，该范例可以稳健地作用于复杂的特征集。因此，这项工作也将有助于其他领域的特征表现出对标准方法具有挑战性的特征。在项目期间开发的工具和语料库将向社区提供。该项目的成果将有助于提高有关使用韵律信息的科学知识，并提高口语理解和对话系统的能力。

The objective of this project is to develop a modeling framework in order to enable extensive use of prosodic information, such as pitch, duration and energy characteristics, in a large class of applications that call for spoken language understanding. For this purpose, prosodic features are extracted from the speech signal over regions defined by automatically detectable events. The result is a variable-length sequence of usually high-dimensional vectors, with mixed discrete and continuous distributions and undefined values. The focus of the project is the search for a transformation that, when applied to the prosodic features, results in a single vector that can adequately represent the important characteristics of the original sequence of prosodic features. The proposed transform is formed by projecting the distribution of the features in a certain sample onto a set of probability distributions represented by dynamic Bayesian networks in a predetermined dictionary.The ultimate goal of the project is the creation of a general probabilistic model-based transform paradigm that can act robustly on complex feature sets. This work will therefore also contribute to other domains where features exhibit characteristics that are challenging for standard approaches. The tools and corpora developed during the project will be made available to the community. The results from this project will contribute to scientific knowledge on the use of prosodic information and increase the capabilities of spoken language understanding and dialog systems.