Variability of Lombard effects under different noise conditions

The variability of Lombard speech under different noise conditions and an adaptation method for the different Lombard speech are discussed. For this purpose, various kinds of Lombard speech are recorded under different conditions of noise injected into 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. The 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. The authors show, finally, that five words are enough for the learning interpolated transformation matrix for unknown noise conditions.