An introduction to Hidden Markov Models
نویسنده
چکیده
Hidden Markov Models (HMM) are commonly defined as stochastic finite state machines. Formally a HMM can be described as a 5-tuple Ω = (Φ,Σ, π, δ, λ). The states Φ, in contrast to regular Markov Models, are hidden, meaning they can not be directly observed. Transitions between states are annotated with probabilities δ, which indicate the chance that a certain state change might occur. These probabilities, as well as the starting probabilities π, are discrete. Every state has a set of possible emissions Σ and discrete/continuous probabilities λ for these emissions . The emissions can be observed, thus giving some information, for instance about the most likely underlying hidden state sequence which led to a particular observation. This is known as the Decoding Problem. Along with the Evaluation and the Learning Problem it is one of three main problems which can be formulated for HMMs. This paper will describe these problems, as well as the algorithms, like the Forward algorithm, for solving them. As HMMs have become of great use in pattern recognition, especially in speech recognition, an example in this field will be given, to help understand where they can be utilized. The paper will start with an introduction to regular Markov Chains, which are the base for HMMs.
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تاریخ انتشار 2007