Samsung Electronics Co., Ltd.

Alireza Dirafzoon

Anil Sunder Yadav

Mohammad Mahdi Moazzami

Zheng Zhou

Pu Song

Aviral Anshu

Abdul Rafay Khalid

JongHo Shin

A method, an electronic device and computer readable medium for dialogue breakdown detection are provided. The method includes obtaining a verbal input from an audio sensor. The method also includes generating a reply to the verbal input. The method additionally includes identifying a local context from the verbal input and a global context from the verbal input, additional verbal inputs previously received by the audio sensor, and previous replies generated in response to the additional verbal inputs. The method further includes identifying a dialogue breakdown in response to determining that the reply does not correspond to the local context and the global context. In addition, the method includes generating sound corresponding to the reply through a speaker when the dialogue breakdown is not identified.

System and method for context-enriched attentive memory network with global and local encoding for dialogue breakdown detection

A method, an electronic device, and computer readable medium is provided. The method includes identifying a set of observable features associated with one or more users. The method also includes generating latent features from the set of observable features. The method additionally includes sorting the latent features into one or more clusters. Each of the one or more clusters represents verbal utterances of a group of users that share a portion of the latent features. The method further includes generating a language model that corresponds to a specific cluster of the one or more clusters. The language model represents a probability ranking of the verbal utterances that are associated with the group of users of the specific cluster.

System and method for language model personalization

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San Jose, CA, United States of America

Alireza Dirafzoon