Samsung Electronics Co., Ltd.

Hoshik Lee

Jihyun Lee

Hodong Lee

Jeong-Hoon Park

Jinwoo Park

Hwidong Na

Heeyoul Choi

Hogyeong Kim

Hyohyeong Kang

Sangsoo Lee

Ju-Pyung Lee

Sang Hyun Yoo

Myung-June Jung

Junhwi Choi

Gae-Won You

Jehun Jeon

Min-Joong Lee

Ki Soo Kwon

Sanghyun Yoo

Young Sang Choi

Inchul Song

YoungSang Choi

Yeha Lee

Hee Youl Choi

Jangsu Lee

Yunhong Min

A processor-implemented model training method and apparatus are provided. The method calculates an entropy of each of a plurality of previously trained models based on training data, selects a previously trained model from the plurality of previously trained models based on the calculated entropy, and trains a target model, distinguished from the plurality of previously trained models, based on the training data and the selected previously trained model.

Apparatus and method with model training

A decoding method includes receiving an input sequence corresponding to an input speech at a current time; and in a neural network (NN) for speech recognition, generating an encoded vector sequence by encoding the input sequence, determining reuse tokens from candidate beams of two or more previous times by comparing the candidate beams of the previous times, and decoding one or more tokens subsequent to the reuse tokens based on the reuse tokens and the encoded vector sequence.

Method and apparatus with decoding in neural network for speech recognition

A translation method includes: selecting a source word from a source sentence; generating mapping information including location information of the selected source word mapped to the selected source word in the source sentence; and correcting a target word, which is generated by translating the source sentence, based on location information of a feature value of the target word and the mapping information.

Translation method and apparatus, and translation system

A processor-implemented method includes performing speech recognition of a speech signal, generating a plurality of first candidate sentences as a result of the performing of the speech recognition, identifying a respective named entity in each of the plurality of first candidate sentences, determining a standard expression corresponding to the identified respective named entity using phonemes of the corresponding named entity, determining whether to replace the identified named entity in each of the plurality of first candidate sentences with the determined standard expression based on a similarity between the named entity and the standard expression corresponding to the named entity and determining a plurality of second candidate sentences based on the determination result; and outputting a final sentence selected from the plurality of second candidate sentences.

Method and apparatus with speech processing

A processor implemented method to update a sentence generation model includes: generating a target sentence corresponding to a source sentence using a first decoding model; calculating reward information associated with the target sentence using a second decoding model configured to generate a sentence in an order different from an order of the sentence generated by the first decoding model; and generating an updated sentence generation model by resetting a weight of respective nodes in the first decoding model based on the calculated reward information.

Method of updating sentence generation model and sentence generating apparatus

A model training method and apparatus is disclosed, where the model training method acquires a recognition result of a teacher model and a recognition result of a student model for an input sequence and trains the student model such that the recognition result of the teacher model and the recognition result of the student model are not distinguished from each other.

Model training method and apparatus

A speech generation method and apparatus are disclosed. The speech generation method includes obtaining, by a processor, a linguistic feature and a prosodic feature from an input text, determining, by the processor, a first candidate speech element through a cost calculation and a Viterbi search based on the linguistic feature and the prosodic feature, generating, at a speech element generator implemented at the processor, a second candidate speech element based on the linguistic feature or the prosodic feature and the first candidate speech element, and outputting, by the processor, an output speech by concatenating the second candidate speech element and a speech sequence determined through the Viterbi search.

Method and apparatus for generating speech

A processor-implemented electronic device input method includes: identifying input items configured to receive information on a displayed screen, by performing either one or both of a layout analysis and an image analysis with respect to the screen; tagging one of the input items with a text token extracted from a speech recognition result of a speech signal; and inputting the tagged text token into the one of the input items.

Device and method with input

Disclosed is a dialogue management method and apparatus. The dialogue management method includes sequentially resolving an application domain to provide a service to a user, a function appropriate for an intent of a user from among functions of the application domain, and at least one slot to perform the function, adaptively determining an expression scheme of a dialogue management interface depending on a progress of the sequentially resolving, and displaying the dialogue management interface.

Dialogue management method based on dialogue management framework and apparatus thereof

A model training method and apparatus is disclosed, where the model training method acquires first output data of a student model for first input data and second output data of a teacher model for second input data and trains the student model such that the first output data and the second output data are not distinguished from each other. The student model and the teacher model have different structures.

A speech recognition method includes receiving a sentence generated through speech recognition, calculating a degree of suitability for each word in the sentence based on a relationship of each word with other words in the sentence, detecting a target word to be corrected among the words in the sentence based on the degree of suitability for each word, and replacing the target word with any one of candidate words corresponding to the target word.

Method and apparatus for speech recognition

A processor-implemented text-to-speech method includes determining, using a sub-encoder, a first feature vector indicating an utterance characteristic of a speaker from feature vectors of a plurality of frames extracted from a partial section of a first speech signal of the speaker, and determining, using an autoregressive decoder, into which the first feature vector is input as an initial value, from context information of the text, a second feature vector of a second speech signal in which a text is uttered according to the utterance characteristic.

Method and apparatus with text-to-speech conversion

A processor-implemented machine translation method includes translating a source sentence expressed in a source language into a target language to determine a target sentence, determining, based on a reliability of the target sentence, whether the target sentence is appropriate as a translation result of the source sentence, and re-determining a target sentence corresponding to the source sentence in response to a result of the determining being that the target sentence is inappropriate as the translation result.

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Seongnam-si, South Korea

Hoshik Lee