A feature extraction method using subband based periodicity and aperiodicity decomposition with noise robust frontend processing for automatic speech recognition

Abstract This paper proposes a frontend processing technique that employs a speech feature extraction method called Subband based Periodicity and Aperiodicity DEcomposition (SPADE), and examines its validity for automatic speech recognition in noisy environments. SPADE divides speech signals into subband signals, which are then decomposed into their periodic and aperiodic features, and uses both features as speech feature parameters. SPADE employs independent periodicity estimation within each subband and periodicity–aperiodicity decomposition design based on a parallel distributed processing technique motivated by the human speech perception process. Unlike other speech features, this decomposition of speech into two characteristics provides information about periodicities and aperiodicities, and thus allows the utilization of the robustness exhibited by periodic features without losing certain essential information included in aperiodic features. This paper first introduces an implementation of SPADE that operates in the frequency domain, and then examines the validity of combining SPADE with speech enhancement methods. For this examination, we combine SPADE with noise compensation methods that operate in the frequency domain and cepstral normalization methods. In addition, we employ an energy parameter calculation method based on the SPADE framework. An evaluation with the AURORA-2J noisy continuous digit speech recognition database (Japanese AURORA-2) shows that SPADE combined with adaptive Wiener filtering, cepstral normalization, and the energy parameter achieves average word accuracy rates of 82.58% with clean training and 92.55% with multicondition training. These rates are higher than those achieved with ETSI WI008 advanced DSR frontend processing (77.98% and 91.01%, respectively) whose speech feature parameter is based on conventional Mel-frequency cepstral coefficients. By comparison with ETSI WI008 advanced DSR frontend, the proposed method reduces word error rates by 20.9% with clean training and 17.2% with multicondition training. These results confirmed that SPADE combined with noise reduction methods can increase robustness in the presence of noise.