Word category prediction based on neural network

Abstract

This paper proposes a new method of the word category prediction for the speech recognition system. In order to improve the speech recognition results, not only the acoustical information but also certain linguistic information is needed. World category prediction is a very effective method to implement an accurate word recognition system. Traditional statistical approaches require considerable training data to estimate the probabilities of word sequences, and many parameters to memorize probabilities. And it is difficult to predict unseen data which does not include the training data. To solve this problem, NETgram, which is the neural network for word category prediction, is proposed. The performance of the NETgram is comparable to that of the statistical model although the NETgram requires fewer parameters than the statistical model. Also the NETgram performs effectively for unknown data, i.e., the NETgram interpolates sparse training data. Results of analyzing the NETgram show that the NETgram learns linguistic structure from training data. The results of applying the NETgram to HMM English word recognition show that the NETgram improves the word recognition rate from 81.0% to 86.9%.

Keywords:

• Neural network
• Text analysis
• Word category prediction

Keywords:

• C.R. Categories
• I.2.7.
• F.3.2.