We describe a generalization of the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) which is able to encode prior information that state transitions are more likely between "nearby" states. This is accomplished by defining a similarity function on the state space and scaling transition probabilities by pair-wise similarities, thereby inducing correlations among the transition distributions. We present an augmented data representation of the model as a Markov Jump Process in which: (1) some jump attempts fail, and (2) the probability of success is proportional to the similarity between the source and destination states. This augmentation restores conditional conjugacy and admits a simple Gibbs sampler. We evaluate the model and inference method on a speaker diarization task and a "harmonic parsing" task using four-part chorale data, as well as on several synthetic datasets, achieving favorable comparisons to existing models.

本文介绍了一个对层级狄利克雷过程隐藏马尔可夫模型 (HDP-HMM) 的拓展，它可以编码关于状态转换更可能发生在“相邻”的状态之间的先验信息，并通过在状态空间上定义相似度函数和通过成对相似度缩放转移概率，从而在转移分布之间引入相关性。该模型的数据增强表示形式被提出来，作为马尔可夫跳跃过程，其中有些跳跃尝试失败，而成功的概率与源状态和目标状态之间的相似度成正比。这种增强实现条件共轭并且简单地允许使用 Gibbs 抽样器。我们在扬声器日化任务和使用四部合唱数据的“和声分析”任务以及几个合成数据集上对模型和推理方法进行了评估，并与现有模型进行了有利的比较。

具有相似性偏置转移的无限隐马尔可夫模型