Improving environmental robustness of speech recognition using neural networks

This paper presents a method for improving speech recognition in noisy environment by using neural networks. Two multilayer perceptrons (MLPs) are used. The first MLP minimises the difference between noisy and clean speech and the second one measures the degree of noise in the speech signal and adjusts the time interval between subsequent frames of the processed speech signal accordingly. If we use the technique presented in this paper as a pre-processing stage of a speech recognition system we can extend the application of the system to different environments without re-training it. We need only to train the preprocessing stage with a small portion of noisy data which is created by conducting part of the original clean speech database used for training the speech recognizer through the desired environment. There is no need for creating a new database in the desired working environment. Our method was tested on a vowel spotting system, and is trained with two well known databases: TIMIT and NTIMIT. The evaluation of the system through a vowel spotting process, shows a significant improvement of the recognition rate of the system.