While acoustic analysis methods have become a commodity in voice emotion research, experiments that attempt not only to describe but to computationally manipulate expressive cues in emotional voice and speech have remained relatively rare. We give here a nontechnical overview of voice-transformation techniques from the audio signal-processing community that we believe are ripe for adoption in this context. We provide sound examples of what they can achieve, examples of experimental questions for which they can be used, and links to open-source implementations. We point at a number of methodological properties of these algorithms, such as being specific, parametric, exhaustive, and real-time, and describe the new possibilities that these open for the experimental study of the emotional voice.

尽管声学分析方法已成为语音情感研究中的一种商品，但是尝试不仅描述而且以计算方式操纵情感语音和语音中的表达线索的实验仍然相对较少。在这里，我们对音频信号处理社区的语音转换技术进行了非技术性的概述，我们认为在这种情况下已经可以采用。我们提供了他们可以实现的良好示例，可以使用它们的实验性问题示例，以及到开源实现的链接。我们指出了这些算法的许多方法学特性，例如特定的，参数的，详尽的和实时的，并描述了这些方法为情感语音的实验研究带来的新可能性。