Sentence examples for tested sentence from inspiring English sources

They often skip it after Mrs. Wheeler's tested sentence, but perhaps not as often.

where the emotion/gender score(T, i) is the returned probability value of the GMM classifier for the models trained for each emotion/gender category and the tested sentence T (an input vector of features obtained from this sentence).

When the number of features obtained from analysis of the tested sentence is less than 70, the resulting score produced by the GMM classifier is unstable, non-repeatable, and classification contains a lot of errors.

Figure 14 Influence of limited length of feature vector on stability of the GMM emotion classification process; obtained scores (upper set of graphs), determined class of emotion (bottom set); feature sets P3_8, P3_12, and P3_16, N gmix = 6, N iter = 1200; tested sentence expressed by the female speaker in joyous style.

Figure 15 Influence of incorrectly chosen GMM model of gender type on stability of the emotion classification; obtained scores (upper set of graphs), determined class of emotion (bottom set); feature set P3, N gmix = 6, N iter = 1200; tested sentence expressed in neutral speaking style by the male speaker (left two graphs), and by the female speaker (right graphs).

Figure 17 Stability test of the GMM emotion classifier for female gender; obtained scores (upper set of graphs), and finally determined class of emotion (bottom set); feature set P3, N gmix = 6, N iter = 1200; tested sentence expressed by the female speaker in neutral and emotional styles.

Three task conditions were tested: sentences (plausibility judgment), single words (concrete/abstract judgment), non-words (homophonic judgment).

After the neutral sentence, either the next trial was initiated (66.67%) or a three-alternative multiple-choice question was inserted to test sentence comprehension (33.33%).

Van Rooij and Plomp (1991), for example, tested the sentence set developed by Plomp and Mimpen (1979), designed to be relatively homogeneous, and found differences of up to 4 dB in the SRT in noise between individual sentences.

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Phone lattices were generated for each test sentence by HTK.