Sentence examples for test speaker from inspiring English sources

Data belonging to test speaker is not included in the reference set, thus there is no need for a priori information about all emotional categories of the test speaker.

Three significance tests were applied, including the matched pair (MP) sentence segment (word error) test, the signed paired (SI) comparison test (speaker word accuracy rate), and the Wilcoxon (WI) signed rank test (speaker word accuracy rate).

In Table 3, we summarize the recognition results for the CU-Move task in which each test speaker had an average of approximately 60 utterances.

10 utterances (about 130 seconds on average) for each test speaker were kept aside for speaker adaptation and the remaining utterances formed the test set.

In other words, ℓ 1 norm minimization ignores those speakers from the background set who are least similar in comparison to the test speaker by forcing their weights to zero to ensure sparseness.

For offline VTLN experiments reported in this paper, however, we used all the available data from each test speaker to estimate the best warps in an offline setting (i.e., warp factors are not estimated for every single utterance).

Speaker adaptation in speech recognition task helps to reduce the mismatch between the training and test speakers and results in improved recognition performance for test speakers.

In speech recognition task, speaker adaptation tries to reduce mismatch between the training and test speakers.

Using the normalised models, warping factors were estimated for each of the test speakers from the adaptation set utterances.

Of course, the study used only native English speakers, and Kita says it's crucial to test speakers of other languages to see if the findings hold.

Consequently, CNNs should have a smaller performance drop for test speakers whose voice is different from that of the training speakers.