Wavelet Energy-Based Support Vector Machine for Noisy Word Boundary Detection With Speech Recognition Application

Wavelet Energy-Based Support Vector Machine for Noisy Word Boundary Detection With Speech Recognition Application

Word boundary detection in variable noise-level environments by support vector machine (SVM) using Low-band Wavelet Energy (LWE) and Zero Crossing Rate (ZCR) features is proposed in this paper. The Wavelet Energy is derived based on Wavelet transformation; it can reduce the affection of noise in a speech signal. With the inclusion of ZCR, we can robustly and effectively detect word boundary from noise with only two features. For detector design, a Gaussian-kernel SVM is used. The proposed detection method is applied to detection word boundaries for an isolated word recognition system in variable noisy environments. Experiments with different types of noises and various signal-to-noise ratios are performed. The results show that using the LWE and ZCR parameters-based SVM, good performance is achieved. Comparison with another robust detection method has also verified the performance of the proposed method.