To Investigate The Accuracy Of The Vector Quantization 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. There are various models used for voice conversion such as hidden Markov model (HMM), artificial neural network (ANN), vector quantization (VQ) 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. These correspondences are formed by segmenting the spectral vectors of the source and target speakers into clusters using VQ-based clustering. VQ reduces the computation amount and memory size drastically. The objective of the paper is to investigate the effect of VQ based transformation function estimation on the closeness of the transformed speech towards.

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