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minimum phone error.
Fig. 5 The progress of phone error rates during training.
We then applied the minimum phone error (MPE) discriminative training technique described in [17].
We also examine the patterns of phone error reduction and look at the cost-performance tradeoffs.
Fig. 3 Phone error rate as a function of the input context size.
The phone error rates are shown as a function of the pooling size.
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In general, missing data were attributed to 4 main deficiencies: Training shortcomings/misunderstanding of the software Miscommunication with partners who manage contact tracers Misuse of phones Errors in the reference database from the paper system To address the first 3 deficiency categories, we changed hardware and SIM card management protocols and conducted targeted refresher trainings.
This GMM-HMM word matcher has been trained from a set of training data and the decoding of these speech data to learn from the phone recognition errors.
We also touched on the issue of automatic phone recognition errors that can affect the accuracy of our speech-rate measures (see Section 5.4).
Possible implications of phone-recognition errors are discussed in Section 5. We compute the number of speech units per second over the entire duration of a single patient's free-response session.
He accepted the upgrade, but within seconds the phone was displaying "error 53" and was, in effect, dead.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com