Jacobian Joint Adaptation to Noise, Channel and Vocal Tract Length

Abstract

A new Jacobian approach that linearly decomposes the composite of additive noise, multiplicative noise (channel transfer function) and speaker's vocal tract length, and adapts the acoustic model parameters simultaneously to these factors is proposed in this paper. Due to the fact that these factors non-linearly degrade the observed features for speech recognition, existing approaches fail to adapt the acoustic models adequately. Approximating the nonlinear operation by a linear model enables to employ the least square error estimation of the factors and adapt the acoustic model parameters with small amount of speech samples. Speech recognition experiments on ATR isolated word database demonstrate significant reduction of error rates, which supports the effectiveness of the proposed scheme.