Isolated Word Recognition Using Normalized Teager Energy Cepstral Features

A robust feature extraction technique using Teager Energy Operator (TEO) for Isolated Word Recognition (IWR) has been proposed in this paper. A feature extraction algorithm is motivated by the enhanced discrimination capability TEO that estimates the true energy of the source of a resonance. The robustness is further added using Cepstral Mean Normalization (CMN) on the estimated features. The robust features are computed from the speech signal of a given frame through a series of steps. First, the short time spectrum of each frame of speech signal is calculated. Second, the frame spectrum is passed through a Mel scaled triangular filter bank. Then, the average of absolute values of sequence obtained after applying TEO on each filter output is estimated. Finally, the cepstral coefficients are extracted by applying discrete cosine transform on the estimated averages. These coefficients are further normalized using CMN to get the final features denoted as Normalized Teager Energy Coefficient (NTEC) features. The effectiveness of this technique has been tested on TI-20 isolated word database in presence of white noise. The experimental results show the superiority of the proposed technique over conventional MFCC, Spectral Subtraction (SS) and CMN methods.