Multi-Condition Training for Unknown Environment Adaptation in Robust ASR Under Real Conditions

Abstract

Automatic speech recognition (ASR) systems frequently work in a noisy environment. As they are often trained on clean speech data, noise reduction or adaptation techniques are applied to decrease the influence of background disturbance even in the case of unknown conditions. Speech data mixed with noise recordings from particular environment are often used for the purposes of model adaptation. This paper analyses the improvement of recognition performance within such adaptation when multi-condition training data from a real environment is used for training initial models. Although the quality of such models can decrease with the presence of noise in the training material, they are assumed to include initial information about noise and consequently support the adaptation procedure. Experimental results show significant improvement of the proposed training method in a robust ASR task under unknown noisy conditions. The decrease by 29 % and 14 % in word error rate in comparison with clean speech training data was achieved for the non-adapted and adapted system, respectively.