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In this case, the acoustic models (hidden Markov models) are embedded into the finite-state transducers, and the translation of a source utterance is the result of a (Viterbi) search on the integrated network.
Therefore, the spectrogram of each source utterance can be represented as: X={A}^{(X)}.
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Whatever its source, the utterance struck me with the impact of a moment of splendid transcendence.
In DeLillo's White Noise, the narrator's daughter lists car brands in her sleep, in a manner that approaches incantation: "Toyota Corolla, Toyota Celica, Toyota Cressida ... Whatever its source, the utterance struck me with the impact of a moment of splendid transcendence".
For visual features, DCT of lip motion images of the source speaker's utterance is used.
For visual features, 50-dimensions of the DCT coefficient of the lip motion images of the source speaker's utterance were used.
Instead, a matching process of the source and target utterances is performed as follows.
An HMM is trained for each source, where the utterances of the sources are scaled to have a known equal energy.
In the transformation stage, the test utterances of source speaker are pre-processed in the similar way as the training stage to get the separate feature vectors for magnitude and phase information of filtered coefficients.
Some of these reasons can be stated: environmental conditions are (usually) rapidly changing or highly degraded, acquisition processes are not always under control, incriminated people exhibit low degree of cooperativeness, etc., inducing a wide range of variability sources on speech utterances.
Our training set includes 50 parallel utterances obtained from each of the speaker pairs and a separate set of 25 utterances of each source speaker are used to evaluate the system.
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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