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We propose Gaussian Mixture Model (GMM)-based emotional voice conversion using spectrum and prosody features. In recent years, speech recognition and synthesis techniques have been developed, and an emotional voice conversion technique is required for synthesizing more exp ressive voices. The common emotional conversion was based on transformation of neutral prosody to emotional prosody by using huge speech corpus. In this paper, we convert a neutral voice to an emot ional vo ice using GMMs. GMM-based spectrum conversion is widely used to modify non linguistic informat ion such as voice characteristics while keep ing linguistic information unchanged. Because the conventional method converts either prosody or voice quality (spectrum), so me emot ions are not converted well. In our method, both prosody and voice quality are used for converting a neutral voice to an emotional voice, and it is able to obtain more expressive voices in comparison with conventional methods, such as prosody or spectrum conversion.

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Many recent advances in computer vision are the result of a healthy competition among researchers on high quality, task-specific, benchmarks. After a decade of active research, zero-shot learning (ZSL) models accuracy on the Imagenet benchmark remains far too low to be considered for practical object recognition applications. In this paper, we argue that the main reason behind this apparent lack of progress is the poor quality of this benchmark. We highlight major structural flaws of the current benchmark and analyze different factors impacting the accuracy of ZSL models. We show that the actual classification accuracy of existing ZSL models is significantly higher than was previously thought as we account for these flaws. We then introduce the notion of structural bias specific to ZSL datasets. We discuss how the presence of this new form of bias allows for a trivial solution to the standard benchmark and conclude on the need for a new benchmark. We then detail the semi-automated construction of a new benchmark to address these flaws.