Abstract: The present disclosure provides method and a computer system for tracking audio streaming media. The method includes: after streaming media information matching a currently played audio stream segment is identified, determining whether the streaming media information matches a next audio stream segment, and when the streaming media information matches the next audio stream segment, directly returning a result; or when the streaming media information does not match the next audio stream segment, performing streaming media information matching again for the next audio stream segment, which occupies less memory, improves a calculation speed, and reduces calculation complexity of matching the streaming media information.

Abstract: The present disclosure provides method and a computer system for tracking audio streaming media. The method includes: after streaming media information matching a currently played audio stream segment is identified, determining whether the streaming media information matches a next audio stream segment, and when the streaming media information matches the next audio stream segment, directly returning a result; or when the streaming media information does not match the next audio stream segment, performing streaming media information matching again for the next audio stream segment, which occupies less memory, improves a calculation speed, and reduces calculation complexity of matching the streaming media information.

Abstract: A matching method and system for audio content, includes: obtaining a first audio frame and a second audio frame from an audio clip to be matched, wherein the first audio frame and the second audio frame are audio frames in successive; converting the first audio frame into a first group of sub-bands and converting the second audio frame into a second group of sub-bands; converting the first group of sub-bands into a first group of sub-hash tables and converting the second group of sub-bands into a second group of sub-hash tables; separately comparing the first group of sub-hash tables and the second group of sub-hash tables with the audio clips stored in a database and obtaining a first group of candidate audio and a second group of candidate audio; determining a matching result by selecting from the first group of candidate audio and the second group of candidate audio.

Abstract: The present invention provides a method and apparatus for training a duration prediction model, method and apparatus for duration prediction, method and apparatus for speech synthesis. Said method for training a duration prediction model, comprising: generating an initial duration prediction model with a plurality of attributes related to duration prediction and at least part of possible attribute combinations of said plurality of attributes, in which each of said plurality of attributes and said attribute combinations is included as an item; calculating importance of each said item in said duration prediction model; deleting the item having the lowest importance calculated; re-generating a duration prediction model with the remaining items; determining whether said re-generated duration prediction model is an optimal model; and repeating said step of calculating importance and the following steps, if said duration prediction model is determined as not optimal model.

Abstract: The present invention provides a method and apparatus for training F0 and pause prediction model, method and apparatus for F0 and pause prediction, method and apparatus for speech synthesis.

Abstract: The present invention provides a method and apparatus for training a duration prediction model, method and apparatus for duration prediction, method and apparatus for speech synthesis. Said method for training a duration prediction model, comprising: generating an initial duration prediction model with a plurality of attributes related to duration prediction and at least part of possible attribute combinations of said plurality of attributes, in which each of said plurality of attributes and said attribute combinations is included as an item; calculating importance of each said item in said duration prediction model; deleting the item having the lowest importance calculated; re-generating a duration prediction model with the remaining items; determining whether said re-generated duration prediction model is an optimal model; and repeating said step of calculating importance and the following steps, if said duration prediction model is determined as not optimal model.