Speech feature extraction method using subband-based periodicity and nonperiodicity decomposition

Abstract

This paper proposes a speech feature extraction method that utilizes periodicity and nonperiodicity for robust automatic speech recognition. The method was motivated by the auditory comb filtering hypothesis proposed in speech perception research. The method divides input signals into subband signals, which it then decomposes into their periodic and nonperiodic components using comb filters independently designed in each subband. Both features are used as feature parameters. This representation exploits the robustness of periodicity measurements as regards noise while preserving the overall speech information content. In addition, periodicity is estimated independently in each subband, providing robustness as regards noise spectrum bias. The framework is similar to that of a previous study [Jackson et al., Proc. of Eurospeech. (2003), pp. 2321-2324], which is based on cascade processing motivated by speech production. However, the proposed method differs in its design philosophy, which is based on parallel distributed processing motivated by speech perception. Continuous digit speech recognition experiments in the presence of noise confirmed that the proposed method performs better than conventional methods when the noise in the training and test data sets differs.