Samsung Electronics Co., Ltd.

Universitéde Montreal

University of Montreal

Sanghyun Yoo

Ki Soo Kwon

Minyoung Mun

Young-Seok Kim

Inchul Song

Junhwi Choi

Jeong-Hoon Park

Sangsoo Lee

Hoshik Lee

Taesup Kim

Kyoung-Gu Woo

Jehun Jeon

Hodong Lee

Young Sang Choi

Yoshua Bengio

GyuBum Han

Misuk Kim

Junyoung Chung

A method and apparatus for personalizing a speech recognition model is disclosed. The apparatus may obtain feedback data that is a result of recognizing a first speech input of a user using a trained speech recognition model, determine whether to update the speech recognition model based on the obtained feedback data, and selectively update, dependent on the determining, the speech recognition model based on the feedback data.

Method and apparatus with a personalized speech recognition model

Disclosed is a method and apparatus for processing a speech. The method includes obtaining context information from a speech signal of a user using a neural network-based encoder, determining, based on the context information, attention information corresponding to a segment included in the speech signal, and recognizing, based on the attention information, the segment by decoding a portion of the context information identified as corresponding to the segment.

Method and apparatus with speech processing

Disclosed is a method and apparatus for processing a speech. The method includes obtaining context information from a speech signal of a user using a neural network-based encoder, determining intent information of the speech signal based on the context information, determining, based on the context information, attention information corresponding to a segment included in the speech signal, and determining, based on the attention information, a segment value of the segment by recognizing, using a decoder, a portion of the context information identified as corresponding to the segment.

A method and apparatus with natural language processing is disclosed. The method includes determining a first similarity between an input sentence of a user and a select first database query sentence and dependent on a determination that the first similarity fails to meet a first threshold, determining a second similarity between a portion of the input sentence, less than all of the input sentence, and a select second database query sentence, and in response to the second similarity meeting a second threshold, outputting a response sentence corresponding to the second database query sentence as a response to the input sentence.

Method and apparatus with natural language processing

A word embedding method and apparatus and a word search method are provided, wherein the word embedding method includes training a word embedding model based on characteristic information of a chemical substance, and acquiring an embedding vector of a word representing the chemical substance from the word embedding model, wherein the word embedding model is configured to predict a context word of an input word.

Word embedding method and apparatus, and word search method

A speech recognition method includes obtaining an acoustic sequence divided into a plurality of frames, and determining pronunciations in the acoustic sequence by predicting a duration of a same pronunciation in the acoustic sequence and skipping a pronunciation prediction for a frame corresponding to the duration.

Speech recognition method and apparatus

A speech recognition method and apparatus, including implementation and/or training, are disclosed. The speech recognition method includes obtaining a speech signal, and performing a recognition of the speech signal, including generating a dialect parameter, for the speech signal, from input dialect data using a parameter generation model, applying the dialect parameter to a trained speech recognition model to generate a dialect speech recognition model, and generating a speech recognition result from the speech signal by implementing, with respect to the speech signal, the dialect speech recognition model. The speech recognition method and apparatus may perform speech recognition and/or training of the speech recognition model and the parameter generation model.

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 speech signal recognition method, apparatus, and system. The speech signal recognition method may include obtaining by or from a terminal an output of a personalization layer, with respect to a speech signal provided by a user of the terminal, having been implemented by input of the speech signal to the personalization layer, the personalization layer being previously trained based on speech features of the user, implementing a global model by providing the obtained output of the personalization layer to the global model, the global model being configured to output a phonemic signal indicating a phoneme included in the speech signal through the global model being previously trained based on speech features common to a plurality of users, and re-training the personalization layer based on the phonemic signal output from the global model, where the personalization layer and the global model collectively represent an acoustic model.

Speech signal recognition system and method

Provided are an apparatus and method for processing a multidimensional string query. Using the apparatus and method, a search of a multidimensional multi-attribute string may be rapidly processed on the basis of a multidimensional n-gram index.

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Seoul, South Korea

Sanghyun Yoo