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It is difficult to determine whether differences were statistically significant due to the re-use of data in each test run.
We think that the reduced performance improvements in the KPOW task are mainly resulted from the reason that because the adaptation is performed on a single utterance basis, the amount of adaptation data in each test utterance becomes not enough to fully adapt the much larger number of acoustic models in this large vocabulary task.
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To answer this question, we split the data in each task into training and test sets and tested the similarity between each test set and the seven training sets, as described in the methods.
Data was logged in each test vehicle during the whole trial and was analyzed for 3 one-month periods: Before activating the system, after short time use and after long time use.
Before the command, 5-seconds of data were collected in each test.
Raw data for the experimental auditory tests are displayed in Fig. 3. Auditory performance data for each test are summarized in Table 2.
We analyzed the data in each ROI by testing the performance of a linear support vector classifier trained to predict near versus far depth based on voxel activation patterns evoked when participants viewed targets in the D, M, DM, and D-M conditions.
We classified the data on each test type in the primary studies according to commercial brands.
The behavioral data for each test were analyzed in separate analyses of variance with lesion as the between-subjects factor.
The behavioral data for each test were analyzed in separate analyses of variance (ANOVAs) with lesion as the between-subjects factor.
Changes over time for individual patients can be interpreted in the context of normative data available for each test in the BCA.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com