Patent ID: 9558738
Date: 2017-01-31
CPC Classifications: G10L

Claim:
1. A method comprising: receiving training data; identifying non-contextual lexical-level features in the training data; inferring sentence-level features from the training data using a sentence classifier seeded with portions of the training data; estimating a first language model based on a sentence length by linearly interpolating subsets of the training data with a 3-gram language model, wherein the subsets are determined by length of sentences in the training data; generating a set of decision trees by node-splitting according to the non-contextual lexical-level features and the sentence-level features; decorrelating training vectors, according to the training data, for each decision tree in the set of decision trees to approximate full-covariance Gaussian models; adapting a pre-existing acoustic model for use in speech recognition on received speech according to the training data, the set of decision trees, and the training vectors, to yield an adapted acoustic model; combining the first language model with a second language model using interpolation weights and a frequency of carrier phrases in the training data, to yield a combined language model; and performing the speech recognition on the received speech using the adapted acoustic model and the combined language model.