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Finally, we notice that using the Viterbi decoder at the receiver side enhance the performance in terms of BER, where we obtain 4 dB of a gain in the case of 4-QAM and L = 1.
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And then, the total bits are decoded by using the Viterbi soft decoding algorithm.
For PRED-TMBB, the predictions were performed using the Viterbi and Posterior Decoding algorithms.
These probabilities are decoded using the Viterbi algorithm.
This decoding stage can be done in an efficient way using the Viterbi algorithm.
The stretch where they match is aligned with the audio using the Viterbi algorithm.
It can be determined in polynomial time using the Viterbi algorithm [21].
Once the CNF model is trained, we can calculate the optimal alignment using the Viterbi algorithm (Viterbi, 1967).
Next, we predicted constrained elements by using the Viterbi algorithm.
The resulting complexity is 3I + 1 times that of a single use of the Viterbi decoder.
Hence, assuming that the EM algorithm converges after I iterations, the complexity as compared to a single use of the Viterbi decoder is I + 1 times for algorithm A 2, whereas it is 3I + 1 times when the APP computation is according to the BCJR algorithm.
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