Abstract: A speech recognition method includes acquiring speech data, inputting a speech feature matrix of the speech data to a speech recognition model, the speech feature matrix being used for representing time-domain and frequency-domain features of the speech data, performing attention encoding on the speech feature matrix through the speech recognition model to obtain an encoded matrix, the encoded matrix including multiple encoded vectors, and decoding the multiple encoded vectors in the encoded matrix according to positions of the multiple encoded vectors in the encoded matrix to output a character string corresponding to the speech data, a decoding sequence of the multiple encoded vectors being related to the positions of the multiple encoded vectors in the encoded matrix.

Abstract: Methods and computer systems for audio search on a social networking platform are disclosed. While running a social networking application, a computer system receives a first audio input from a user of the computer system and then generates a first audio confusion network from the first audio input. After comparing the first audio confusion network with one or more second audio confusion networks, each corresponding to a second audio input associated with one of a plurality of participants of a chat session of the social networking application, the computer system identifies at least one second audio input corresponding to the at least one second audio confusion network that matches the first audio confusion network and displays a portion of the chat session including a visual icon representing the identified second audio input on a display of the computer system.

Abstract: Methods and computer systems for audio search on a social networking platform are disclosed. While running a social networking application, a computer system receives a first audio input from a user of the computer system and then generates a first audio confusion network from the first audio input. After comparing the first audio confusion network with one or more second audio confusion networks, each corresponding to a second audio input associated with one of a plurality of participants of a chat session of the social networking application, the computer system identifies at least one second audio input corresponding to the at least one second audio confusion network that matches the first audio confusion network and displays a portion of the chat session including a visual icon representing the identified second audio input on a display of the computer system.

Abstract: Methods and computer systems for audio search on a social networking platform are disclosed. The method includes: while running a social networking application, receiving a first audio input from a user of the computer system, the first audio input including one or more search keywords; generating a first audio confusion network from the first audio input; determining whether the first audio confusion network matches at least one of one or more second audio confusion networks, wherein a respective second audio confusion network was generated from a corresponding second audio input associated with a chat session of which the user is a participant; and identifying a second audio input corresponding to the at least one second audio confusion network that matches the first audio confusion network, wherein the identified second audio input includes the one or more search keywords that are included in the first audio input.

Abstract: Systems and methods are provided for speech recognition. For example, audio characteristics are extracted from acquired voice signals; a syllable confusion network is identified based on at least information associated with the audio characteristics; a word lattice is generated based on at least information associated with the syllable confusion network and a predetermined phonetic dictionary; and an optimal character sequence is calculated in the word lattice as a speech recognition result.

Abstract: Methods and computer systems for audio search on a social networking platform are disclosed. The method includes: while running a social networking application, receiving a first audio input from a user of the computer system, the first audio input including one or more search keywords; generating a first audio confusion network from the first audio input; determining whether the first audio confusion network matches at least one of one or more second audio confusion networks, wherein a respective second audio confusion network was generated from a corresponding second audio input associated with a Chat session of which the user is a participant; and identifying a second audio input corresponding to the at least one second audio confusion network that matches the first audio confusion network, wherein the identified second audio input includes the one or more search keywords that are included in the first audio input.

Abstract: A method and an apparatus are provided for retrieving keyword. The apparatus configures at least two types of language models in a model file, where each type of language model includes a recognition model and a corresponding decoding model; the apparatus extracts a speech feature from the to-be-processed speech data; performs language matching on the extracted speech feature by using recognition models in the model file one by one, and determines a recognition model based on a language matching rate; and determines a decoding model corresponding to the recognition model; decoding the extracted speech feature by using the determined decoding model, and obtains a word recognition result after the decoding; and matches a keyword in a keyword dictionary and the word recognition result, and outputs a matched keyword.

Abstract: A method is implemented at a computer to determine that certain information content is composed or compiled in a specific language selected among two or more similar languages. The computer integrates a first vocabulary list of a first language and a second vocabulary list of a second language into a comprehensive vocabulary list. The integrating includes analyzing the first vocabulary list in view of the second vocabulary list to identify a first vocabulary sub-list that is used in the first language, but not in the second language. The computer then identifies, in the information content, a plurality of expressions that are included in the comprehensive vocabulary list, and a subset of expressions that are included in the first vocabulary sub-list. Upon a determination that a total frequency of occurrence of the subset of expressions meets predetermined occurrence criteria, the computer determines that the information content is composed in the first language.

Abstract: A method and an apparatus are provided for retrieving keyword. The apparatus configures at least two types of language models in a model file, where each type of language model includes a recognition model and a corresponding decoding model; the apparatus extracts a speech feature from the to-be-processed speech data; performs language matching on the extracted speech feature by using recognition models in the model file one by one, and determines a recognition model based on a language matching rate; and determines a decoding model corresponding to the recognition model; decoding the extracted speech feature by using the determined decoding model, and obtains a word recognition result after the decoding; and matches a keyword in a keyword dictionary and the word recognition result, and outputs a matched keyword.

Abstract: This application discloses a method implemented of recognizing a keyword in a speech that includes a sequence of audio frames further including a current frame and a subsequent frame. A candidate keyword is determined for the current frame using a decoding network that includes keywords and filler words of multiple languages, and used to determine a confidence score for the audio frame sequence. A word option is also determined for the subsequent frame based on the decoding network, and when the candidate keyword and the word option are associated with two distinct types of languages, the confidence score of the audio frame sequence is updated at least based on a penalty factor associated with the two distinct types of languages. The audio frame sequence is then determined to include both the candidate keyword and the word option by evaluating the updated confidence score according to a keyword determination criterion.

Abstract: A method and an apparatus are provided for retrieving keyword. The apparatus configures at least two types of language models in a model file, where each type of language model includes a recognition model and a corresponding decoding model; the apparatus extracts a speech feature from the to-be-processed speech data; performs language matching on the extracted speech feature by using recognition models in the model file one by one, and determines a recognition model based on a language matching rate; and determines a decoding model corresponding to the recognition model; decoding the extracted speech feature by using the determined decoding model, and obtains a word recognition result after the decoding; and matches a keyword in a keyword dictionary and the word recognition result, and outputs a matched keyword.

Abstract: This application discloses a method implemented of recognizing a keyword in a speech that includes a sequence of audio frames further including a current frame and a subsequent frame. A candidate keyword is determined for the current frame using a decoding network that includes keywords and filler words of multiple languages, and used to determine a confidence score for the audio frame sequence. A word option is also determined for the subsequent frame based on the decoding network, and when the candidate keyword and the word option are associated with two distinct types of languages, the confidence score of the audio frame sequence is updated at least based on a penalty factor associated with the two distinct types of languages. The audio frame sequence is then determined to include both the candidate keyword and the word option by evaluating the updated confidence score according to a keyword determination criterion.

Abstract: Systems and methods are provided for voice identification. For example, audio characteristics are extracted from acquired voice signals; a syllable confusion network is identified based on at least information associated with the audio characteristics; a word lattice is generated based on at least information associated with the syllable confusion network and a predetermined phonetic dictionary; and an optimal character sequence is calculated in the word lattice as an identification result.

Abstract: A method is implemented at a computer to determine that certain information content is composed or compiled in a specific language selected among two or more similar languages. The computer integrates a first vocabulary list of a first language and a second vocabulary list of a second language into a comprehensive vocabulary list. The integrating includes analyzing the first vocabulary list in view of the second vocabulary list to identify a first vocabulary sub-list that is used in the first language, but not in the second language. The computer then identifies, in the information content, a plurality of expressions that are included in the comprehensive vocabulary list, and a subset of expressions that are included in the first vocabulary sub-list. Upon a determination that a total frequency of occurrence of the subset of expressions meets predetermined occurrence criteria, the computer determines that the information content is composed in the first language.