Bayesian Learning of a Hierarchical Representation of Language from Raw Speech

这个项目的目标是从原始语音输入中学习语言的层次表示。在最低层，语音的声学构建块，音素或类似的子词单元，被发现并为它们学习统计模型，而下一层则涉及词汇构建块，单词的发现，并学习它们的概率。最后，语义可解释的词类别将建立从他们。特别关注的事实，即在自然语言中的词汇原则上是无限的，声学信号表现出极大的变化。这两个问题都将在贝叶斯范式中处理。为了使词汇量随着输入数据量的增长而增长，将采用非参数贝叶斯统计，特别是基于Dirichlet和Pitman-Yor过程的统计，其中参数的数量不需要预先指定。口语输入的变化，导致在声学单元发现阶段的歧义，占通过避免过早的决定音素身份和治疗声学和词汇变量的联合概率模型，将开发有效的推理技术。虽然我们设想在语音处理领域的几个应用程序，联合或迭代层次贝叶斯学习框架的开发也将是其他顺序和高度可变的感官输入数据的学习问题的兴趣。

The goal of this project is to learn a hierarchical representation of a language from raw speech input. At the lowest layer the acoustic building blocks of speech, the phonemes or similar sub-word units, are discovered and statistical models learned for them, while the next layer is concerned with the discovery of the lexical building blocks, the words, and learning their probabilities. Finally, semantically interpretable word categories will be built from them. Particular focus is placed on the facts, that the vocabulary in natural languages is in principle unlimited and that the acoustic signal exhibits extreme variability. Both issues will be approached in a Bayesian paradigm. To allow the vocabulary to grow with the amount of input data nonparametric Bayesian statistics, in particular those based on the Dirichlet and the Pitman-Yor processes, will be employed, where the number of parameters need not be specified in advance. The variability of the spoken input, leading to ambiguities at the acoustic unit discovery stage, is accounted for by avoiding premature decisions on the phoneme identity and treating acoustic and lexical variables in a joint probabilistic model, for which efficient inference techniques will be developed. While we envision several applications in the realm of speech processing, the joint or iterative hierarchical Bayesian learning framework to be developed will also be of interest to learning problems on other sequential and highly variable sensory input data.