Abstract: The present disclosure provides a sound-collecting method, apparatus, device and computer storage medium, wherein the method comprises: a sound-collecting apparatus collecting first sound data while playing a preset speech section; collecting sound data of a user following and reading the speech section; subjecting the sound data of following and reading the speech section to interference removal processing by using a sound interference coefficient to obtain second sound data, wherein the sound interference coefficient is determined with the speech section and the first sound data; obtaining training data for speech synthesis by using the second sound data. The quality of the collected sound data can be improved in a manner provided by the present disclosure.

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: A model evaluation method includes obtaining M first audio signals synthesized by using a first to-be-evaluated speech synthesis model, and obtaining N second audio signals generated through recording; performing voiceprint extraction on each of the M first audio signals to obtain M first voiceprint features; performing voiceprint extraction on each of the N second audio signals to obtain N second voiceprint features; clustering the M first voiceprint features to obtain K first central features; clustering the N second voiceprint features to obtain J second central features; counting the cosine distances between the K first central features and the J second central features to obtain a first distance; and evaluating the first to-be-evaluated speech synthesis model based on the first distance.

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: A Cr—Mn—N austenitic heat-resistant steel is provided. The heat-resistant steel comprises, in weight percentage, carbon 0.20% to 0.50%, silicon 0.50% to 2.00%, manganese 2.00% to 5.00%, phosphorus less than 0.04%, sulphur less than 0.03%, chromium 20.00% to 27.00%, nickel 6.00% to 8.00%, molybdenum less than 0.50%, niobium less than 0.60%, tungsten less than 0.60%, vanadium less than 0.15%, nitrogen 0.30% to 0.60%, zirconium less than 0.10%, cobalt less than 0.10%, yttrium less than 0.10%, boron less than 0.20%, with the balance iron. The heat-resistant steel has high temperature strength, high thermal conductivity, low thermal expansion coefficient, good dimensional stability, good ductility, heat resistance, impact resistance, and low production costs, and meets the requirements for high performance engines.

Abstract: The present disclosure provides a sound-collecting method, apparatus, device and computer storage medium, wherein the method comprises: a sound-collecting apparatus collecting first sound data while playing a preset speech section; collecting sound data of a user following and reading the speech section; subjecting the sound data of following and reading the speech section to interference removal processing by using a sound interference coefficient to obtain second sound data, wherein the sound interference coefficient is determined with the speech section and the first sound data; obtaining training data for speech synthesis by using the second sound data. The quality of the collected sound data can be improved in a manner provided by the present disclosure.

Abstract: A Cr—Mn—N austenitic heat-resistant steel is provided. The heat-resistant steel comprises, in weight percentage, carbon 0.20% to 0.50%, silicon 0.50% to 2.00%, manganese 2.00% to 5.00%, phosphorus less than 0.04%, sulphur less than 0.03%, chromium 20.00% to 27.00%, nickel 6.00% to 8.00%, molybdenum less than 0.50%, niobium less than 0.60%, tungsten less than 0.60%, vanadium less than 0.15%, nitrogen 0.30% to 0.60%, zirconium less than 0.10%, cobalt less than 0.10%, yttrium less than 0.10%, boron less than 0.20%, with the balance iron. The heat-resistant steel has high temperature strength, high thermal conductivity, low thermal expansion coefficient, good dimensional stability, good ductility, heat resistance, impact resistance, and low production costs, and meets the requirements for high performance engines.