Abstract:
A machine translation method includes converting a source sentence written in a first language to language-independent information using an encoder for the first language, and converting the language-independent information to a target sentence corresponding to the source sentence and written in a second language different from the first language using a decoder for the second language. The encoder for the first language is trained to output language-independent information corresponding to the target sentence in response to an input of the source sentence.
Abstract:
A speech recognition method and apparatus are provided, in which the speech recognition apparatus may recognize a user feature and a speech recognition environment, determine a speech recognition speed for performing speech recognition based on one of the recognized user feature and the speech recognition environment, and perform the speech recognition based on the determined speech recognition speed.
Abstract:
A method and apparatus for training a neural network language model, and a method and apparatus for recognizing speech data based on a trained language model are provided. The method of training a language model involves converting, using a processor, training data into error-containing training data, and training a neural network language model using the error-containing training data.
Abstract:
An apparatus and a method to estimate a degree of user fatigue to video content are disclosed including a feature value calculating unit and a fatigue degree calculating unit. The feature value calculating unit is configured to calculate feature values corresponding to image and sound features in a video content. The fatigue degree calculating unit is configured to calculate a degree of user fatigue to the video content by applying the feature values to a fatigue degree estimation model.
Abstract:
A method, apparatus, and system with physical property prediction inference and/or training is provided. A processor-implemented method includes predicting physical properties of a target material using a machine learning model provided an input that is based on a target feature vector, where the target feature vector corresponds to the target material, where the machine learning model is configured to predict the physical properties of the target material based on a multi-dimensional space that is dependent on feature vectors representing respective structures of materials and relation information between the materials.
Abstract:
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.
Abstract:
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.
Abstract:
A sentence generating method includes: generating a corresponding word set of a source word set generated based on a source sentence; generating words by performing decoding based on feature vectors generated through encoding of the source sentence; adjusting a probability of at least one of the generated words based either one or both of the source word set and the corresponding word set; and selecting character strings from different character strings including each of the generated words based on the adjusted probability and the probability as unadjusted.
Abstract:
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.
Abstract:
A sentence generating apparatus includes an encoder configured to generate a first sentence embedding vector by applying trained result data to a first paraphrased sentence of an input sentence, an extractor configured to extract verification sentences in a preset range from the generated first sentence embedding vector, and a determiner configured to determine a similarity of the first paraphrased sentence to the input sentence based on comparing the verification sentences to the input sentence.