Weighted training for speech under Lombard Effect for speaker recognition

The presence of Lombard Effect in speech is proven to have severe effects on the performance of speech systems, especially speaker recognition. Varying kinds of Lombard speech are produced by speakers under influence of varying noise types [1]. This study proposes a high-accuracy classifier using deep neural networks for detecting various kinds of Lombard speech against neutral speech, independent of the noise levels causing the Lombard Effect. Lombard Effect detection accuracies as high as 95.7% are achieved using this novel model. The deep neural network based classification is further exploited by validation based weighted training of robust i-Vector based speaker identification systems. The proposed weighted training achieves a relative EER improvement of 28.4% over an i-Vector baseline system, confirming the effectiveness of deep neural networks in modeling Lombard Effect.