| 摘要 |
<p>PROBLEM TO BE SOLVED: To differentially and robustly learn a model parameter for an error correction model by using features such as long text or a topic.SOLUTION: Language model learning unit 23 learns a language model for calculating connection probability for a following word by recursive neural network with output of topic feature amount extracted from words in present speech and words in prior speech and a hidden layer as inputs from static text data. Alignment unit 32 aligns a correct word line to voice data and calculates output of the hidden layer of the recursive neural network to respective words in the correct word line. A voice recognition unit 33 recognizes voice of voice data and calculates output of the hidden layer to respective words of voice recognition result. An error correction model learning unit 35 statistically learns an error correction model based on linguistic feature of a word composing an aligned correct word line and linguistic feature of the output of the hidden layer and the word composing the voice recognition result and the output of the hidden layer.</p> |
