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Section 4 has the experimental results that compare the proposed multi-stream HMM with existing HMM approaches.
Training sets ranging from 50 to 50,000 read-half alignments were used to train the HMM with two different sets of initial HMM values.
A GMM can be viewed as a single-state HMM with a Gaussian mixture observation density, or an ergodic Gaussian observation HMM with fixed, equal transition probabilities[38].
It is based on a Hidden Markov model (HMM) with architecture obeying those proteins' construction principles.
In order to demonstrate the learning process of an HMM with diffusion maps, we take a simple HMM with 3 states as an example.
The POBDS model is a special case of Hidden Markov Model (HMM) with Boolean state variables.
There is one left-to-right HMM with three emitting states for each AE and speech.
Silence is also modeled by a three-state HMM with six Gaussian mixtures in each state.
2-state HMM with minimum duration constraints (threshold-free, unsupervised, no training).
Each phoneme is represented by a left-to-right HMM with three states.
The acoustic modeling is performed using a five-state HMM with full connected state transitions.
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