Nonlinear Spectral Transformations for Robust Speech Recognition

Recently, a nonlinear transformation of autocorrelation coefficients named Phase AutoCorrelation (PAC) coefficients has been considered for feature extraction \cite{ikbal03}. PAC based features show improved robustness to additive noise as a result of two operations, performed during the computation of PAC, namely energy normalization and inverse cosine transformation. In spite of the improved robustness achieved for noisy speech, these two operations lead to some degradation in recognition performance for clean speech. In this paper, we try to alleviate this problem, first by introducing the energy information back into the PAC based features, and second by studying alternatives to inverse cosine function. Simply appending the frame energy as an additional coefficient in the PAC features has resulted in noticeable improvement in the performance for clean speech. Study of alternatives to inverse cosine transformation leads to a conclusion that linear transformation is the best for clean speech, while nonlinear functions help to improve robustness in noise.