Samsung Electronics Co., Ltd.

Universitéde Montreal

University of Montreal

Inchul Song

Sang Hyun Yoo

Hwidong Na

Sanghyun Yoo

Minyoung Mun

Hoshik Lee

Taesup Kim

Hodong Lee

Ki Soo Kwon

Young Sang Choi

YoungSang Choi

Yoshua Bengio

Youngki Park

Junyoung Chung

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.

Provided is a method and device to personalize a speech recognition model, the device that personalizes a speech recognition model by identifying a language group corresponding to a user, and generating a personalized speech recognition model by applying a group scale matrix corresponding to the identified language group to at least a layer of a speech recognition model.

Device and method to personalize speech recognition model

Disclosed is a speech recognition method and apparatus, wherein the apparatus acquires first outputs from sub-models in a recognition model based on a speech signal, acquires a second output including values corresponding to the sub-models from a classification model based on the speech signal, and recognizes the speech signal based on the first outputs and the second output.

A translation method and apparatus may respectively perform or include: using one or more processors, plural different translation processes, in parallel, for a source sentence in a first language, including encoding, to generate respective feature vectors, the source sentence in each of two or more translation processes of the plural translation processes or the source sentence and a variation of the source sentence in respective translation processes of the plural translation processes, and decoding each of the respective feature vectors to generate respective plural candidate sentences in a second language; and selecting a final sentence in the second language from the respective plural candidate sentences in the second language.

Parallel processing-based translation method and apparatus

A training method of an acoustic model includes constructing window-level input speech data based on a speech sequence; inputting the window-level input speech data to an acoustic model; calculating a sequence level-error based on an output of the acoustic model; acquiring window-level errors based on the sequence level-error; and updating the acoustic model based on the window-level errors.

Method and apparatus for training acoustic model

An apparatus for calculating acoustic score, a method of calculating acoustic score, an apparatus for speech recognition, a method of speech recognition, and an electronic device including the same are provided. An apparatus for calculating acoustic score includes a preprocessor configured to sequentially extract audio frames into windows and a score calculator configured to calculate an acoustic score of a window by using a deep neural network (DNN)-based acoustic model.

Apparatus and method of acoustic score calculation and speech recognition using deep neural networks

An apparatus and method for evaluating quality of an automatic translation is disclosed. An apparatus for evaluating quality of automatic translation includes a converter which converts an automatic translation and a reference translation of an original text to a first distributed representation and a second distributed representation, respectively, using a distributed representation model and a quality evaluator which evaluates quality of automatic translation data based on similarity between the first distributed representation and the second distributed representation.

Apparatus and method for evaluating quality of automatic translation and for constructing distributed representation model

A user adaptive speech recognition method and apparatus are provided. A speech recognition method includes extracting an identity vector representing an individual characteristic of a user from speech data, implementing a sub-neural network by inputting a sub-input vector including at least the identity vector to the sub-neural network, determining a scaling factor based on a result of the implementing of the sub-neural network, implementing a main neural network, configured to perform a recognition operation, by applying the determined scaling factor to the main neural network and inputting the speech data to the main neural network to which the determined scaling factor is applied, and indicating a recognition result of the implementation of the main neural network.

Growing community of inventors

Suwon-si, South Korea

Inchul Song