Eye gaze is an important means for controlling interaction and coordinating the participants' turns smoothly. We have studied how eye gaze correlates with spoken interaction and especially focused on the combined effect of the speech signal and gazing to predict turn taking possibilities. It is well known that mutual gaze is important in the coordination of turn taking in two-party dialogs, and in this article, we investigate whether this fact also holds for three-party conversations. In group interactions, it may be that different features are used for managing turn taking than in two-party dialogs. We collected casual conversational data and used an eye tracker to systematically observe a participant's gaze in the interactions. By studying the combined effect of speech and gaze on turn taking, we aimed to answer our main questions: How well can eye gaze help in predicting turn taking? What is the role of eye gaze when the speaker holds the turn? Is the role of eye gaze as important in three-party dialogs as in two-party dialogue? We used Support Vector Machines (SVMs) to classify turn taking events with respect to speech and gaze features, so as to estimate how well the features signal a change of the speaker or a continuation of the same speaker. The results confirm the earlier hypothesis that eye gaze significantly helps in predicting the partner's turn taking activity, and we also get supporting evidence for our hypothesis that the speaker is a prominent coordinator of the interaction space. Such a turn taking model could be used in interactive applications to improve the system's conversational performance.

To address questions of whether endogenous BDNF acts differentially on inhibitory and excitatory neurons, and through what routes, we used chimera culture of cerebral cortical neurons derived from BDNF-/- mice and another type of transgenic mice that express green fluorescence protein and BDNF. Presynaptic BDNF transferred to both types of neurons, GABA-synthesizing enzyme-positive and -negative neurons. The latter neurons were confirmed to be glutamatergic with immunocytochemistry. Dendritic development of the former inhibitory neurons was promoted by endogenous BDNF transferred from presynaptic, excitatory neurons. In contrast, dendritic development of excitatory neurons was not related to the presence or absence of presynaptic BDNF, suggesting that BDNF acts on inhibitory neurons through an anterograde, transsynaptic route so as to promote dendritic development, whereas this is not the case in excitatory neurons.

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This paper addresses unsupervised speaker indexing for discussion audio archives. In discussions, the speaker changes frequently, thus the duration of utterances is very short and its variation is large, which causes significant problems in applying conventional methods such as model adaptation and Variance-BIC (Bayesian Information Criterion) methods. We propose a flexible framework that selects an optimal speaker model (GMM or VQ) based on the BIC according to the duration of utterances. When the speech segment is short, the simple and robust VQbased method is expected to be chosen. while GMM will be reliably trained for long segments. For a discussion archive having a total duration of 10 hours, it is demonstrated that the proposed method achieves higher indexing performance than that of conventional methods.

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