An improved dynamic time warping algorithm employing nonlinear median filtering

This paper proposes an improved dynamic time warping (DTW) algorithm with a nonlinear median filtering (NMF). Recognition accuracy of conventional DTW algorithms are less than the hidden Markov model (HMM) by same voice activity detection (VAD) and noise-reduction with running spectrum filtering (RSF) and dynamic range adjustment (DRA). For analyzing some incorrect results, unlike in conventional DTW, we do not use the minimum distance to recognize. we employ NMF and seek the median distance of the every reference word with the unknown speech waveform. All recognition accuracy of conventional DTW algorithms are improved much more by NMF. The recognition accuracy of Itakura's DTW algorithm is the best. Its recognition accuracy similar to that of the HMM approach in 10 dB and 20 dB white noise.