Effect of Mfcc Based Features for Speech Signal Alignments

The fundamental techniques used for man-machine communication include Speech synthesis, speech recognition, and speech transformation. Feature extraction techniques provide a compressed representation of the speech signals. The HNM analyses and synthesis provides high quality speech with less number of parameters. Dynamic time warping is well known technique used for aligning two given multidimensional sequences. It locates an optimal match between the given sequences. The improvement in the alignment is estimated from the corresponding distances. The objective of this research is to investigate the effect of dynamic time warping on phrases, words, and phonemes based alignments. The speech signals in the form of twenty five phrases were recorded. The recorded material was segmented manually and aligned at sentence, word, and phoneme level. The Mahalanobis distance (MD) was computed between the aligned frames. The investigation has shown better alignment in case of HNM parametric domain. It has been seen that effective speech alignment can be carried out even at phrase level.