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Thus far, voice conversion studies are mainly focused on the conversion of spectrum. However, speaker identity is also characterized by its prosody features, such as fundamental frequency (F0) and energy contour. We believe that with a better understanding of speaker dependent/independent prosody features, we can devise an analytic approach that addresses voice conversion in a better way. We consider that speaker dependent features reflect speaker's individuality, while speaker independent features reflect the expression of linguistic content. Therefore, the former is to be converted while the latter is to be carried over from source to target during the conversion. To achieve this, we provide an analysis of speaker dependent and speaker independent prosody patterns in different temporal scales by using wavelet transform. The centrepiece of this paper is based on the understanding that a speech utterance can be characterized by speaker dependent and independent features in its prosodic manifestations. Experiments show that the proposed prosody analysis scheme improves the prosody conversion performance consistently under the sparse representation framework.