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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sqmhhstt3jaevo25kzxbew33h4" style="color: black;">APSIPA Transactions on Signal and Information Processing</a>
Voice conversion aims to change a source speaker's voice to make it sound like the one of a target speaker while preserving linguistic information. Despite the rapid advance of voice conversion algorithms in the last decade, most of them are still too complicated to be accessible to the public. With the popularity of mobile devices especially smart phones, mobile voice conversion applications are highly desirable such that everyone can enjoy the pleasure of high-quality voice mimicry and people<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1017/atsip.2018.23">doi:10.1017/atsip.2018.23</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ldtuy5hjjbgjdni2qo4yikfmwu">fatcat:ldtuy5hjjbgjdni2qo4yikfmwu</a> </span>
more »... with speech disorders can also potentially benefit from it. Due to the limited computing resources on mobile phones, the major concern is the time efficiency of such a mobile application to guarantee positive user experience. In this paper, we detail the development of a mobile voice conversion system based on the Gaussian mixture model (GMM) and the weighted frequency warping methods. We attempt to boost the computational efficiency by making the best of hardware characteristics of today's mobile phones, such as parallel computing on multiple cores and the advanced vectorization support. Experimental evaluation results indicate that our system can achieve acceptable voice conversion performance while the conversion time for a five-second sentence only takes slightly more than one second on iPhone 7.
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