Traditional Dynamic Time Warping (DTW) technique find similarities between two one-dimensional time series sequence. Initially, in earlier decades, DTW was not preferred because of its computational complexity. However, due to the evolution of computing power, this has been revisited for spoken keyword detection recently. Conventional spectral features such as Mel-Frequency Cepstral Coefficients (MFCC) and contemporary wavelet features are multi-dimensional in nature which are used in speech recognition. In this work, a new strategy of DTW is proposed to work with multi-dimensional feature vector in calculating the local distance matrix. Additionally, a faster approach is specified to find the similarity in the global distance matrix. The proposed methods are evaluated with MFCC and wavelet features on a connected TIDIGITS corpus for spoken keyword detection system. Experimental results prove that there is an improvement in reduction of computational complexity compared to traditional DTW. Also, contemporary wavelet feature based spoken keyword detection system gave better detection accuracy than MFCC based spoken keyword detection system in the noisy environment.