1. A voice data analyzing device comprising: a speaker model deriving unit which derives speaker models as models each specifying character of voice of each speaker from voice data including a plurality of utterances to each of which a speaker label as information for identifying a speaker has been assigned; and a speaker co-occurrence model deriving unit which derives a speaker co-occurrence model as a model representing the strength of a co-occurrence relationship in which a plurality of speakers appear in a same conversation, among the speakers from session data obtained by segmenting the voice data in units of sequences of conversation by use of the speaker models derived by the speaker model deriving unit.
1. A voice data analyzing device comprising: a speaker model deriving unit which derives speaker models as models each specifying character of voice of each speaker from voice data including a plurality of utterances to each of which a speaker label as information for identifying a speaker has been assigned; and a speaker co-occurrence model deriving unit which derives a speaker co-occurrence model as a model representing the strength of a co-occurrence relationship in which a plurality of speakers appear in a same conversation, among the speakers from session data obtained by segmenting the voice data in units of sequences of conversation by use of the speaker models derived by the speaker model deriving unit. 7. The voice data analyzing device according to claim 1 , wherein the speaker models and the speaker co-occurrence model are learned by executing iterative calculation based on the maximum likelihood criterion, the maximum a posteriori probability criterion or the Bayesian criterion regarding the voice data and the speaker labels.