Abstract: An audio recognizing method, including: performing acoustic feature prediction on the audio to be recognized to obtain first audio prediction result and an acoustic feature reference quantity for predicting an audio recognition result; obtaining second audio prediction result based on the acoustic feature reference quantity; and determining the audio recognition result of the audio to be recognized based on the first audio prediction result and the second audio prediction result, the audio recognition result including unvoiced sound or voiced sound. When determining that the audio is unvoiced sound or voiced sound, the first audio prediction result obtained by performing acoustic feature prediction on the audio to be recognized is used, and the second audio prediction result is obtained in combination with other acoustic feature reference quantities, thereby making the determination result of unvoiced sound or voiced sound of the audio more accurate, to improve the audio quality in speech processing.

Abstract: A method for generating a speech package, an electronic device and a storage medium The method includes: determining a number of texts to be displayed and a speech recording condition based on a type of a recording mode selection control in response to the recording mode selection control being triggered; acquiring speech data with an amount matched with the number based on the speech recording condition; sending the speech data to a server; and acquiring a speech package generated by the server using the speech data.

Abstract: This application discloses a prosodic pause prediction method, a prosodic pause prediction device and an electronic device. The specific implementation scheme includes: obtaining a first matrix by mapping a to-be-tested text sequence through a trained embedding layer, where the to-be-tested text sequence includes a to-be-tested input text and an identity of a to-be-tested speaker; inputting the first matrix into a trained attention model, and determining a semantic representation matrix by the trained attention model; and, performing prosodic pause prediction based on the semantic representation matrix and outputting a prosodic pause prediction result of each word in the to-be-tested input text.

Abstract: This application discloses a prosodic pause prediction method, a prosodic pause prediction device and an electronic device. The specific implementation scheme includes: obtaining a first matrix by mapping a to-be-tested text sequence through a trained embedding layer, where the to-be-tested text sequence includes a to-be-tested input text and an identity of a to-be-tested speaker; inputting the first matrix into a trained attention model, and determining a semantic representation matrix by the trained attention model; and, performing prosodic pause prediction based on the semantic representation matrix and outputting a prosodic pause prediction result of each word in the to-be-tested input text.