具有多个时间独立流的隐马尔可夫模型
我试图找出当基础状态相同但观察结果在时间上不相关时是否有一种将两个 HMM 合并为一个的好方法。
我有两个独立的观察流描述相同的隐藏状态空间。每个观察流的底层顺序保持不变，但它们不会同时发出。
例如，假设我有两个单独的扬声器大声朗读同一段文本的录音，其中隐藏状态空间成为文本中的字母，而每个音频中的音素流构成了观察空间。每个扬声器单独录制音频，并在朗读时使用不同的节奏。
我可以清楚地使用每个说话者独立地对文本进行预测，并在事后尝试协调结果......但我感觉将观察流组合成单个 HMM 可能会产生更好的结果。
有谁知道协调这个问题的好方法？
I'm trying to figure out if there is a good way to merge two HMMs into one, when the underlying states are the same, but the observations aren't temporally linked.
I have two independent observation streams describing the same hidden state space. The underlying order of each observation stream remains the same, but they are not emitted at the same time.
For instance, say I have audio recordings of two separate speakers reading aloud the same passage of text, where the hidden state space becomes the letters in the text, while the stream of phonemes from each audio comprise the observation space. Each speaker records the audio separately, and use a different cadence when reading.
I can clearly make a prediction of the text using each speaker independently, and try and reconcile the results after the fact... but I sense that combining the observation streams into a single HMM may produce a better result.
Does anyone know a good way to reconcile this?
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合并状态需要首先对齐这些流......即某种对数似然优化。
但可以使用来自多个流的统计来预测“观察结果” - 现代数据压缩器基本上就是这样做的。
例如。请参阅http://www.mattmahoney.net/dc/dce.html#Section_432
Merging the states would require aligning these streams first... ie some kind of log-likelihood optimization.
But its possible to use statistics from multiple streams to predict the "observations" - modern data compressors basically do just that.
Eg. see http://www.mattmahoney.net/dc/dce.html#Section_432
我不确定是否有方法可以在将两个 HMM 分别拟合到不同的观察序列后将其合并。
但是存在一种算法可以在多个独立的观察序列上训练一个马尔可夫模型。
例如在论文中对此进行了介绍
“隐马尔可夫模型和语音识别中选定应用程序的教程”
作者：Rabiner
不幸的是，我还没有找到这个算法的实现。
这是我在 stackexchange 上的相应问题： https://stats.stackexchange.com/questions/53256/两个序列一个嗯
I am not sure if there are methods to merge two HMM's after they have each been fitted to different observation sequences.
But there exists an algotihm to train one Markov Model on multiple independent observation sequences.
It is coverered for example in the paper
"A tutorial to Hidden Markov models and selected applications in speech recognition"
by Rabiner
Unfortunately, I haven't yet found an implementiation of this algorithm.
Here is my corresponding question on stackexchange: https://stats.stackexchange.com/questions/53256/two-sequences-one-hmm