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Table 1 Bayesian estimation using grid-based approximation

From: Grid-based approximation for voice conversion in low resource environments

Input: a sequence of states sampled from the observed process x 1:T

Initialization: set the initial weights, \(\left \{w_{0|0}^{k}\right \}_{k=1}^{N_{y}}\), using Eq. (13)

Main iteration: for t=1,…T, perform the following steps:

1. Evaluate the prior weights, \(\left \{w_{t|t-1}^{k}\right \}_{k=1}^{N_{y}}\), using Eq. (10).

2. Evaluate the posterior weights, \(\left \{w_{t|t}^{k}\right \}_{k=1}^{N_{y}}\), using Eq. (11).

3. Evaluate the hidden state, \(\hat {\mathbf {y}}_{t}\), using Eq. (12).

Output: a sequence of the estimated hidden states \({\hat {\mathbf {y}}_{1:T}}\)