Sentence examples for training words from inspiring English sources

With NMF-based VC, there is less distortion as the number of training words increases.

Figure 6 Mel-cepstral distortion as a function of the number of training words.

Figure 6 shows the mel-cepstral distortions in the evaluation set as a function of the number of training words.

Due to the complexity of training the neural network language model, this requires several days or an even longer time period to converge for several million training words.

Mel-cepstral distortion between the target and converted mel-cepstra given by the following equation [29] was used in order to evaluate the size of training words and the weights of the sparsity constraint: MCD ( v conv, v ref ) = ( α / T ) ∑ t = 0 T − 1 ∑ d = 1 24 ( v d conv ( t ) − v d ref ( t ) ) 2 (7) α = 10 2 / ln 10 (8).

So what, pray tell, does a dog's visual acuity have to do with training words like "Stay" and "Come," or helping dogs adjust to other nuances of living along side people?

Compared with word2vec, GloVe requires constructing word word co-occurrence matrix and directly uses it for training word embeddings.

By training word vector with deep learning, each word has their corresponding spatial coordinates in high-dimensional space.

The next step for the Circle Trick is to hold the treat above the hamster's head and say the training word(s).

Do this again until you can hold your finger above the hamster, say the training word(s), and it will turn in a circle.

At t3, trained words were read faster than untrained words [ t(7) = 4.1, P < 0.005].