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Rotation impaired within-modal recognition accuracy in both vision and touch, but not cross-modal recognition accuracy.

Figure 2 shows that object rotation substantially degraded recognition accuracy in the within-modal conditions, but only slightly decreased cross-modal recognition accuracy.

These two factors interacted (F1,23 = 12.58, p = .002) and post-hoc t-tests showed that this was because within-modal recognition accuracy was highly significantly reduced by rotation (t = 7.25, p <.001) while cross-modal recognition accuracy was not (t = 1.66, p = .11) (Figure 2).

A two-way (within- vs. cross-modal, unrotated vs. rotated) repeated-measures analysis of variance RM-ANOVAA) showed that object rotation significantly reduced recognition accuracy (F1,23 = 30.04, p = <.001) and that overall within-modal recognition accuracy was marginally better than overall cross-modal recognition (F1,23 = 4.23, p = .051).051

The average recognition accuracy of 92.2% was recorded.

The recognition accuracy is the average of all repetitions.

The recognition accuracy is the average of the 10 speakers.

The conclusions reached are: (1) the most accurate system is "posterior product", (2) multi-modal recognition has higher accuracy to either modality alone if all visual and tactile training data are pooled together, and (3) in the case of visual impairment, multi-modal recognition "learns faster", i.e. requires fewer training samples to achieve the same accuracy as either other modality.

Among these methods, score-level fusion is the most popular approach because it offers reasonable recognition accuracy and comparatively simple implementation, and thus many researchers in the multi-modal research community work in the score-level fusion field [12 17].

The recognition accuracy is over 94%.

How does deep learning improve speech recognition accuracy?

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