We have been addressing the problem of acquiring attributes of unknown terms through dialogues and previously proposed an approach using the implicit confirmation process. It is crucial for dialogue systems to ask questions that do not diminish the user’s willingness to talk. In this paper, we conducted a user study to investigate user impression for several question types, including explicit and implicit, to acquire lexical knowledge. We clarified the order among the types and found that repeating the same question type annoys the user and degrades user impression even when the content of the questions is correct. We also propose a method for determining whether an estimated attribute is correct, which is included in an implicit question. The method exploits multiple-user responses to implicit questions about the attribute of the same unknown term. Experimental results revealed that the proposed method exhibited a higher precision rate for determining the correctly estimated attributes than when only single-user responses were considered.
This paper presents an unsupervised multichannel method that can separate moving sound sources based on an amortized variational inference (AVI) of joint separation and localization. A recently proposed blind source separation (BSS) method called neural full-rank spatial covariance analysis (FCA) trains a neural separation model based on a nonlinear generative model of multichannel mixtures and can precisely separate unseen mixture signals. This method, however, assumes that the sound sources hardly move, and thus its performance is easily degraded by the source movements. In this paper, we solve this problem by introducing time-varying spatial covariance matrices and directions of arrival of sources into the nonlinear generative model of the neural FCA. This generative model is used for training a neural network to jointly separate and localize moving sources by using only multichannel mixture signals and array geometries. The training objective is derived as a lower bound on the log-marginal posterior probability in the framework of AVI. Experimental results obtained with mixture signals of moving sources show that our method outperformed an existing joint separation and localization method and standard BSS methods.
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