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What is the difference of a feature vector and a 'vector-valued observation' as described here?

The term 'vector-valued' is used in the following context:

"Most state-of-the-art [Automatic Speech Recognition] systems use vector valued observations, which are modeled with Gaussian mixture emission densities."

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Saying that something is vector-valued is simply another way of saying that something is a vector, e.g. in regression model

$$ \mathbf{y} = \mathbf{X}\boldsymbol{\beta} + \boldsymbol{\varepsilon} $$

$\mathbf{y}, \boldsymbol{\beta}, \boldsymbol{\varepsilon}$ are all vectors and $\mathbf{X}$ is a matrix, so $\boldsymbol{\beta} = (\beta_0, \beta_1, \dots, \beta_k)$ is vector-valued parameter.

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