Auditory-based robust speech recognition system for ambient assisted living in smart home

Abstract

An auditory-based feature extraction algorithm is proposed for enhancing the robustness of automatic speech recognition. In the proposed approach, the speech signal is characterized using a new feature referred to as the Basilar-membrane Frequency-band Cepstral Coefficient (BFCC). In contrast to the conventional Mel-Frequency Cepstral Coefficient (MFCC) method based on a Fourier spectrogram, the proposed BFCC method uses an auditory spectrogram based on a gammachirp wavelet transform in order to more accurately mimic the auditory response of the human ear and improve the noise immunity. In addition, a Hidden Markov Model (HMM) is used for both training and testing purposes. The evaluation results obtained using the AURORA 2 noisy speech database show that compared to the MFCC method, the proposed scheme improves the speech recognition rate by 15% on average given speech samples with Siganl-to-Noise Ratios (SNRs) ranging from 0 to 20 dB. Thus, the proposed method has significant potential for the development of robust speech recognition systems for ambient assisted living.