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一种语言的分层表示。在最低层，发现语音的声学构建块，音素或类似的子词单元，并为它们学习统计模型，而下一层关注词汇构建块，单词的发现，以及学习它们的概率。最后，将由它们构建语义上可解释的词类。特别关注的事实是，自然语言的词汇量原则上是无限的，声学信号表现出极大的可变性。这两个问题都将以贝叶斯范式来处理。为了允许词汇量随着输入数据量的增加而增长，将使用非参数贝叶斯统计，特别是基于Dirichlet和Pitman-Yor过程的非参数贝叶斯统计，其中不需要预先指定参数的数量。口语输入的可变性导致在声学单元发现阶段的歧义，这是通过避免对音素身份做出过早决定并在联合概率模型中处理声学和词汇变量来解释的，为此将开发有效的推理技术。虽然我们展望了语音处理领域的几个应用，但即将开发的联合或迭代分层贝叶斯学习框架也将对其他顺序和高度可变的感觉输入数据的学习问题感兴趣。

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.