Feature combination for the task of neural network acoustic model learning

Abstract

A method of feature combination for the problem of neural network acoustic models training is proposed aimed at the quality improvement of speech recognition. Unlike the feeding of a concatenated vector of features of a different nature to the neural network input, the proposed method uses a delayed union at the level of hidden layers. It uses individual input streams for each type of features. Such streams are able to extract specific patterns for each type of features and then combine them on the hidden layer of the neural network acoustic model. The effect of the method on the system quality was studied in the task of Russian conversational telephone speech recognition. The proposed method achieves 0.41% absolute reduction of the word error rate relative to the concatenation of features and 1.35% in comparison with the best system using one type of features. The results of the work can be used to develop automatic speech recognition systems.