Selection of spectral compressive operator for vector Taylor series-based model adaptation in noisy environments

摘要

This letter investigates the impact of spectral compression on the vector Taylor series-based model adaptation algorithm. Unlike mel-frequency cepstral coefficients obtained by the logarithmic compression, the fractional power compression is used for extracting features. Since the relationship between acoustic models for clean and noisy speech depends on nonlinearity of the spectrum, it is important to select an appropriate compressive operator in the model adaptation. In this letter, the dependency of spectral nonlinearity on the speech recognition system is analyzed in various noisy environments. Experimental results confirm that the replacement of the compressive operator improves the performance of the model adaptation.