Optimization of time-frequency masking filters using the minimum classification error criterion

The dynamic cepstrum parameter representing a masked spectrum performed extremely well in continuous speech recognition. This paper proposes a new algorithm for optimizing the dynamic cepstrum lifter array. The masking filter is represented by a set of Gaussian-shaped lifters. The standard deviation and the gain of the Gaussians are trained in order to improve the performance of the time-frequency filter. Parameterizing the lifter shape provides robustness against unknown speech samples. Because of the parameterized lifter´s small degree of freedom, it can avoid over-learning. The gradient descent optimizing algorithm is formulated for both a neural network classifier and an HMM classifier. The optimized dynamic cepstrum successfully improved the speech recognition performance for the speech spoken even in a different speaking style