Patent Document ID: 9396724
Application ID: 14181263

Base Claim:
1. A method of building a speech to text decoder, comprising: at a device having one or more processors and memory: acquiring data samples for building a language model; performing categorized sentence mining in the acquired data samples to obtain mining results comprising a respective set of sentences obtained through the categorized sentence mining for each of a plurality of categories; obtaining categorized training samples based on the mining results; building a text classifier based on the categorized training samples; classifying the data samples using the text classifier to obtain a respective class vocabulary and a respective training corpus for each of a plurality of categories; mining the respective training corpus for each category according to the respective class vocabulary for the category to obtain a respective set of high-frequency language templates; performing training on the respective set of high-frequency language templates for each category to obtain a respective template-based language model for the category; performing training on the respective training corpus for each category to obtain a respective class-based language model for the category; and performing training on the respective class vocabulary for each category to obtain a respective lexicon-based language model, wherein the respective template-based language model, the respective class-based language model, and the respective lexicon-based language model for a given category are language models for a given field, and the method further comprises: building the speech to text decoder according to a previously obtained acoustic model, the respective template-based language model, the respective class-based language model and the respective lexicon-based language model for the given field, and the data samples.

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Claim 3:
3. The method of claim 1 , wherein building the text classifier based on the categorized training samples further comprises: obtaining statistical information on a tf-idf (Term Frequency-Inverse Document Frequency) feature and mutual information for the sentences in the respective training samples for each category; and building the text classifier according to the obtained statistical information.