公开(公告)号：US20230386450A1

公开(公告)日：2023-11-30

申请号：US18303394

申请日：2023-04-19

Applicant: Samsung Electronics Co., Ltd.

Inventor： Brendon Christopher Beachy Eby ,  Suhel Jaber ,  Sai Ajay Modukuri ,  Omar Abdelwahab ,  Ankit Goyal

IPC: G10L15/06 ,  G10L15/183

CPC classification number: G10L15/063 ,  G10L2015/0636 ,  G10L15/183

Abstract: A method includes determining, using at least one processing device of an electronic device, a target embedding vector for each class of a plurality of classes. The method also includes generating, using the at least one processing device, an utterance embedding vector using a pre-trained language model, where the utterance embedding vector represents an input utterance associated with an expected class. The method further includes obtaining, using the at least one processing device, a predicted class associated with the input utterance based on distances of the utterance embedding vector to spatial parameters representing the plurality of classes, where the spatial parameter of each class is based on the target embedding vector associated with that class. In addition, the method includes updating, using the at least one processing device, parameters of the language model based on a difference between the predicted class and the expected class.

公开(公告)号：US20230385546A1

公开(公告)日：2023-11-30

申请号：US18315931

申请日：2023-05-11

Applicant: Samsung Electronics Co., Ltd.

Inventor： Brendon Christopher Beachy Eby ,  Suhel Jaber ,  Sai Ajay Modukuri ,  Omar Abdelwahab ,  Ankit Goyal

IPC: G06F40/284

CPC classification number: G06F40/284

Abstract: A method includes receiving an input utterance that is a continuation of a previous utterance. The method also includes, using a trained Siamese network, determining input utterance embeddings representing tokens from the input utterance, pooling the input utterance embeddings with a context token embedding representing a class associated with the previous utterance to generate a representative input utterance embedding, and determining a representative embedding associated with each of multiple possible classes. Each possible class is associated with first and second threshold boundaries. The method further includes, using the trained Siamese network, determining a similarity score for each possible class based on a distance between the representative input utterance embedding and a selected threshold boundary of the representative embedding for that possible class and identifying a class for the input utterance based on the determined similarity scores. In addition, the method includes performing an action corresponding to the identified class.