Variability of Lombard effects under different noise conditions

In this paper, variability of Lombard speech under different noise conditions and an adaptation method to the different Lombard speech are discussed For this purpose, various kinds of Lombard speech are recorded under different conditions of noise injected inter a earphone with controlled feedback of voice. First, DTW word recognition experiments using clean speech as a reference are performed to show that the higher the noise level becomes the more seriously the utterance is affected. Second, linear transformation of the cepstral feature vector is tested to show that when given enough (more than 100 words) training data, the transformation matrix can be correctly learned for each of the noise conditions. Interpolation of the transfer matrix is then proposed in order to reduce the adaptation parameter and number of training samples. Pie show, finally, that five words art enough for the learning interpolated transformation matrix for unknown noise conditions.