Dictation of multiparty conversation using statistical turn taking model and speaker model

A new speech decoder dealing with multiparty conversation is proposed. Multiparty conversation denotes a situation in which many speakers talk to each other. Almost of all conventional speech recognition systems assume that the input data consist of single speaker´s voice. However, some applications, such as dialogue dictation and voice interfaces for multi-users, have to deal with mixed speakers´ voices. In such a situation, the system has to recognize not only the word sequence of the input speech but also the speaker of each part of them. Therefore, we propose a decoder utilizing not only an acoustic model and language model, which are the resources of a conventional single-user speech decoder, but also a statistic turn taking model and speakers models to recognize speech. This framework realizes simultaneous maximum likelihood estimation of spoken word sequence and the speaker sequence. Experimental results using a TV sports news show that the proposed method reduce the word error rate by 7.7% and speaker error rate by 97.8% compared to the conventional method