The recognition accuracy of cloud speech recognition systems has been improving year after year. However, high recognition accuracy is not ensured when multiple speakers are involved. In many situations with multiple speakers, such as in meetings and conversations, voices overlap; while humans can easily distinguish these voices, existing systems have much more difficulty. In this study, we propose and examine a crowdsourcing-based method for identifying and transcribing multispeaker conversations. Crowdsourcing allows users to request work from an unspecified number of workers via the Internet. We demonstrate the usefulness of the proposed system by comparing its accuracy with existing systems and discuss ways to improve the results.