Sentence examples for articulation score from inspiring English sources

Three scores for each participant's spoken responses during scanning were calculated: a semantic score; an articulation score and a combined semantic and articulation score.

The mean of the semantic and articulation score was calculated to produce a combined score.

Before training, using the combined semantic and articulation score as the measure: t(15) = 13; two-tailed; P < 0.001; 95% confidence interval (CI) = 42.6 59.4.

Three articulation scores are computed.

The three articulation scores are then compared, and this is done in four different conditions.

In all cases, the results show that differences lower than 3.5% among actual and virtual articulation scores were found.

Articulation scores and subjectively judged intelligibility of sentences were obtained in a laboratory situation together with the physical measure of the cross-correlation between the source and the listening point.

To investigate the influence of background noise level in the error among the compared articulation scores, pink noise with two distinct levels is introduced in one of the rooms, both in the actual and virtual tests.

The main conclusions are that the virtual auralizations obtained with the room simulator using artificial neural networks to model the head-related impulse responses are successful and that articulation scores are a reliable metric to validate auralizations.

Articulation scores improved significantly in two of the children, but the FOCUS scores improved by less than the 16 points required for an MCID.

When comparing the combined scores (articulation and semantics) on repeat normal spoken sentence stimuli trials in the patients with the repeat vocoded spoken sentence stimuli in the healthy participants, an independent-samples t-test with equal variances not assumed, showed there was no difference between groups [ t(22.7) = 1.7, P = 0.1].