To Investigate the Accuracy of the Dynamic Time Warping Based Transformation Function for Voice Conversion

Voice conversion involves transformation of speaker characteristics in a speech uttered by a speaker called source speaker so as to generate a speech having voice characteristics of a desired speaker called target speaker. Voice conversion technology is used in many applications namely dubbing, to enhance the quality of the speech, text-to-speech synthesizers, online games, multimedia, music, cross-language speaker conversion, restoration of old audio tapes, cellular applications, and low bit-rate speech coding. There are various models used for voice conversion such as Hidden Markov Model (HMM), Artificial Neural Network (ANN), and Dynamic Time Warping (DTW) based. The quality of transformed speech depends upon the accuracy of the transformation function. For obtaining an accurate transformation function, the alignment of the passages spoken by source and target speakers should be properly aligned. The objective of the paper is to investigate the effect of DTW based transformation function estimation on the closeness of the transformed speech towards the target. Keywords-HMM, DTW, ANN, speech synthesis, voice conversion.