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 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 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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 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.

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.

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 speech recognizing method and apparatus is provided. A speech recognizing method, implementing a speech recognizing model neural network for recognition of a speech, includes determining an attention weight based on an output value output by at least one layer of the speech recognizing model neural network at a previous time of the recognition of the speech, applying the determined attention weight to a speech signal corresponding to a current time of the recognition of the speech, and recognizing the speech signal to which the attention weight is applied, using the speech recognizing model neural network.

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: 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.

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 method performed by a speech recognizing apparatus to recognize speech includes: obtaining a distance from the speech recognizing apparatus to a user generating a speech signal; determining a normalization value for the speech signal based on the distance; normalizing a feature vector extracted from the speech signal based on the normalization value; and performing speech recognition based on the normalized feature vector.

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 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.

Abstract: A method and apparatus for training a language model, include generating a first training feature vector sequence and a second training feature vector sequence from training data. The method is configured to perform forward estimation of a neural network based on the first training feature vector sequence, and perform backward estimation of the neural network based on the second training feature vector sequence. The method is further configured to train a language model based on a result of the forward estimation and a result of the backward estimation.

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.