Abstract: A method, the method includes determining a target segment partially overlapping a preceding segment from a speech signal, determining a target character sequence corresponding to the target segment by decoding the target segment, identifying a first overlapping portion between the target character sequence and a preceding character sequence based on an edit distance, and merging the target character sequence and the preceding character sequence based on the first overlapping portion. A cost applied to the edit distance is determined based on any one or any combination of any two or more of a type of operation performed at the edit distance, whether characters to be operated are located in the first overlapping portion, and whether the characters to be operated match. A portion overlapping the preceding segment in the target segment is greater than or equal to 8.3% of the target segment.

Abstract: A decoding method and apparatus in an artificial neural network for speech recognition. The decoding method in the artificial neural network for speech recognition includes performing a first decoding task of decoding a feature including speech information and at least one token recognized up to current time, using a shared decoding layer included in the artificial neural network, performing a second decoding task of decoding the at least one token, using the shared decoding layer, and determining an output token to be recognized subsequent to the at least one token based on a result of the first decoding task and a result of the second decoding task.

Abstract: A method, the method includes determining a target segment from a speech signal, determining a target character sequence corresponding to the target segment by decoding the target segment, identifying a first overlapping portion between the target character sequence and a preceding character sequence based on an edit distance, and merging the target character sequence and the preceding character sequence based on the first overlapping portion. A cost applied to the edit distance is determined based on any one or any combination of any two or more of a type of operation performed at the edit distance, whether characters to be operated are located in the first overlapping portion, and whether the characters to be operated match.

Abstract: A decoding method and apparatus in an artificial neural network for speech recognition. The decoding method in the artificial neural network for speech recognition includes performing a first decoding task of decoding a feature including speech information and at least one token recognized up to current time, using a shared decoding layer included in the artificial neural network, performing a second decoding task of decoding the at least one token, using the shared decoding layer, and determining an output token to be recognized subsequent to the at least one token based on a result of the first decoding task and a result of the second decoding task.

Abstract: A speech signal processing method and apparatus is disclosed. The speech signal processing method includes receiving an input token that is based on a speech signal, calculating first probability values respectively corresponding to candidate output tokens based on the input token, adjusting at least one of the first probability values based on a priority of each of the first probability values, and processing the speech signal based on an adjusted probability value obtained by the adjusting.

Abstract: A processor implemented natural language processing method and apparatus are provided. The natural language processing method includes converting a natural language phrase into a token vector, calculating a repetition count of the token vector, and generating an input vector by encoding the token vector based on the calculated repetition count and a position of the token vector.

Abstract: A speech signal processing method and apparatus is disclosed. The speech signal processing method includes receiving an input token that is based on a speech signal, calculating first probability values respectively corresponding to candidate output tokens based on the input token, adjusting at least one of the first probability values based on a priority of each of the first probability values, and processing the speech signal based on an adjusted probability value obtained by the adjusting.

Abstract: A method, the method includes determining a target segment partially overlapping a preceding segment from a speech signal, determining a target character sequence corresponding to the target segment by decoding the target segment, identifying a first overlapping portion between the target character sequence and a preceding character sequence based on an edit distance, and merging the target character sequence and the preceding character sequence based on the first overlapping portion. A cost applied to the edit distance is determined based on any one or any combination of any two or more of a type of operation performed at the edit distance, whether characters to be operated are located in the first overlapping portion, and whether the characters to be operated match. A portion overlapping the preceding segment in the target segment is greater than or equal to 8.3% of the target segment.

Abstract: A method, the method includes determining a target segment from a speech signal, determining a target character sequence corresponding to the target segment by decoding the target segment, identifying a first overlapping portion between the target character sequence and a preceding character sequence based on an edit distance, and merging the target character sequence and the preceding character sequence based on the first overlapping portion. A cost applied to the edit distance is determined based on any one or any combination of any two or more of a type of operation performed at the edit distance, whether characters to be operated are located in the first overlapping portion, and whether the characters to be operated match.

Abstract: A processor implemented natural language processing method and apparatus are provided. The natural language processing method includes converting a natural language phrase into a token vector, calculating a repetition count of the token vector, and generating an input vector by encoding the token vector based on the calculated repetition count and a position of the token vector.

Abstract: A decoding method and apparatus in an artificial neural network for speech recognition. The decoding method in the artificial neural network for speech recognition includes performing a first decoding task of decoding a feature including speech information and at least one token recognized up to current time, using a shared decoding layer included in the artificial neural network, performing a second decoding task of decoding the at least one token, using the shared decoding layer, and determining an output token to be recognized subsequent to the at least one token based on a result of the first decoding task and a result of the second decoding task.

Abstract: A language model training method and an apparatus using the language model training method are disclosed. The language model training method includes assigning a context vector to a target translation vector, obtaining feature vectors based on the target translation vector and the context vector, generating a representative vector representing the target translation vector using an attention mechanism for the feature vectors, and training a language model based on the target translation vector, the context vector, and the representative vector.

Abstract: A language model training method and an apparatus using the language model training method are disclosed. The language model training method includes assigning a context vector to a target translation vector, obtaining feature vectors based on the target translation vector and the context vector, generating a representative vector representing the target translation vector using an attention mechanism for the feature vectors, and training a language model based on the target translation vector, the context vector, and the representative vector.

Abstract: A method of recognizing speech is provided. The method includes the operations of (a) dividing first speech that is input to a speech recognizing apparatus into frames; (b) converting the frames of the first speech into frames of second speech by applying conversion rules to the divided frames, respectively; and (c) recognizing, by the speech recognizing apparatus, the frames of the second speech, wherein (b) comprises converting the frames of the first speech into the frames of the second speech by reflecting at least one frame from among the frames that are previously positioned with respect to a frame of the first speech.

Abstract: A method of recognizing speech is provided. The method includes the operations of (a) dividing first speech that is input to a speech recognizing apparatus into frames; (b) converting the frames of the first speech into frames of second speech by applying conversion rules to the divided frames, respectively; and (c) recognizing, by the speech recognizing apparatus, the frames of the second speech, wherein (b) comprises converting the frames of the first speech into the frames of the second speech by reflecting at least one frame from among the frames that are previously positioned with respect to a frame of the first speech.