Sentence examples for estimate language from inspiring English sources
Recurrent neural networks have been applied to estimate language models.
Recent researchers have applied neural networks different architectures to build and estimate language models.
Different approaches have been used to estimate language models from a given corpus.
Therefore, we suggest that all utterances of each target language had better be used to estimate language total variability space.
In addition to feed forward network and the recurrent neural network-based language models architectures convolution neural network (CNN) [19] was applied to estimate language models with inputs to the network in the form of character and output predictions is at the word-level.
A novel self-supervised discriminative training method for estimating language models for automatic speech recognition (ASR) is proposed.
In addition, since tasks originally used for estimating aspect(s) of intelligence (VIQ tests) were used here for estimating language abilities, and some language task performance correlated with measured PIQ, the latent "language" factor revealed by PLS might to some extent also reflect a latent factor of general or specific processes of intelligence.
We estimate the language total variability space by using the dataset shown in Section 5, and we suppose that a given target language's entire set of utterances is regarded as having been belonging to different language.
The perceptron algorithm was used in to estimate discriminative language models which correct errors in the output of ASR systems.
Recently, researchers have used different neural network architectures to estimate the language models from a given corpus using unsupervised learning neural networks capabilities.
Confidence measures can straightforwardly be integrated to estimate the language model by replacing the count with the sum over all occurrences of of their respective confidence measures, that is, (10).
