2024年3月8日 · 隐马尔可夫模型 （HMM）是一种统计模型，用于描述观测序列和隐藏状态序列之间的概率关系。 它通常用于生成观测值的底层系统或过程未知或隐藏的情况，因此它被称为“隐马尔可夫模型”。 它用于根据生成数据的潜在隐藏过程来预测未来的观察结果或对序列进行分类。 HMM由两种类型的变量组成：隐藏状态和观测值。 隐藏状态是生成观测数据的基础变量，但它们不是直接可观测的。 观测值是被测量和观测的变量。 隐藏状态和观测值之间的关系使用概率 …

隐马尔科夫模型是结构最简单的 动态贝叶斯网（dynamic Bayesian network，也被称作有向图模型），HMM是可以用于标注问题的统计数学模型，描述由隐藏的 马尔科夫链 随机生成观测序列的过程，属于 生成模型。 HMM模型在语音识别、自然语言处理、生物信息、模式识别等领域有广泛的应用。 首先看看什么样的问题可以使用HMM模型解决。 使用HMM模型来解决的问题一般有两个特征： 1） 问题是基于序列的，比如时间序列、状态序列。 2 ）问题中有两类数据，一类序列 …

2024年10月8日 · 近期想深入了解语音识别 (ASR)中隐马尔可夫模型 (HMM)和深度 神经网络 -隐马尔可夫 (DNN-HMM)混合模型，但是尽管网络上有许多关于DNN-HMM的介绍，如李宏毅教授的《深度学习人类语言处理》 [1]，一些博主的语音识别系列文章 [2]，斯坦福大学HMM课件 [3]。 但是这些材料要么不够细致完备，要么对初学者来说过于复杂深奥（尤以HMM部分的琳琅满目的概率公式为首）。 因此，笔者在阅读了大量相关资料后希望用深入浅出的方式为大家系统地介绍 …

There is a variant of the notion of DFA with output, for example a transducer such as a gsm (generalized sequen-tial machine), which is widely used in machine learning. This machine model is known as hidden Markov model , for short HMM .

2023年1月27日 · Hidden Markov models (HMMs) are a type of statistical modeling that has been used for several years. They have been applied in different fields such as medicine, computer …

In this section, we discuss the hidden Markov model or HMM, which is a state space model in which the hidden states are discrete, so x t ∈ {1, …, n s}. The observations may be discrete, y t ∈ {1, …, n y}, or continuous, y t ∈ R s n, or some combination, as we illustrate below. More details can be found in e.g., [CMR05, Fra08, Rab89].

Hybrid HMM/NN architectures provide a flexible NN parameterization for the control of model structure and complexity. At the same time, they can capture distributions that, in practice, are inaccessible to single HMMs. The HMM/NN hybrid approach is tested, in its simplest form, by constructing a model of the immunoglobulin protein family.

2024年6月24日 · Hidden Markov Models (HMMs) are effective for analyzing time series data with hidden states. Using Scikit-learn simplifies HMM implementation and training, enabling the discovery of hidden patterns in sequential data.

2013年12月12日 · Deep Neural Network Hidden Markov Models, or DNN-HMMs, are recently very promising acoustic models achieving good speech recognition results over Gaussian mixture model based HMMs (GMM-HMMs). In this paper, for emotion recognition from speech, we investigate DNN-HMMs with restricted Boltzmann Machine (RBM) based unsupervised pre-training, and DNN-HMMs with discriminative pre-training. Emotion ...

In the new framework called hidden neural networks (HNN) the usual HMM probability parameters are replaced by neural network outputs. To ensure a probabilistic interpretation the HNN is normalized globally as opposed to the local normalization enforced …