Voice processing method, apparatus, and device and storage medium
AssigneeTencent Technology (Shenzhen) Company Limited
InventorsWei Xiao, Meng Wang, Shidong Shang, Zurong Wu
Life of this patent
Estimating the term…
Abstract
A voice processing method includes: determining a historical voice frame corresponding to a target voice frame; determining a frequency-domain characteristic of the historical voice frame; invoking a network model to predict the frequency-domain characteristic of the historical voice frame, to obtain a parameter set of the target voice frame, the parameter set including a plurality of types of parameters, the network model including a plurality of neural networks (NNs), and a number of the types of the parameters in the parameter set being determined according to a number of the NNs; and reconstructing the target voice frame according to the parameter set.
Claims19
A voice processing method, comprising:
- determining a historical voice frame corresponding to a target voice frame;
- determining a frequency-domain characteristic of the historical voice frame;
- invoking a network model to predict the frequency-domain characteristic of the historical voice frame, to obtain a parameter set of the target voice frame, the parameter set including a plurality of types of parameters, the network model including a first neural network (NN) and a plurality of second NNs, and invoking the network model comprising:
- invoking the first NN to predict the frequency-domain characteristic of the historical voice frame, to obtain a virtual frequency-domain characteristic of the target voice frame;
- invoking the second NNs to predict the virtual frequency-domain characteristic of the target voice frame, to obtain parameters corresponding to the second NNs;
- and
- establishing the parameter set of the target voice frame according to the parameters respectively corresponding to the plurality of second NNs;
- and reconstructing the target voice frame according to the parameter set.
A voice processing device, comprising:
- at least one memory storing computer program instructions;
- and at least one processor coupled to the at least one memory and configured to execute the computer program instructions and perform:
- determining a historical voice frame corresponding to a target voice frame;
- determining a frequency-domain characteristic of the historical voice frame;
- invoking a network model to predict the frequency-domain characteristic of the historical voice frame, to obtain a parameter set of the target voice frame, the parameter set including a plurality of types of parameters, the network model including a first neural network (NN) and a plurality of second NNs, and invoking the network model comprising:
- invoking the first NN to predict the frequency-domain characteristic of the historical voice frame, to obtain a virtual frequency-domain characteristic of the target voice frame;
- invoking the second NNs to predict the virtual frequency-domain characteristic of the target voice frame, to obtain parameters corresponding to the second NNs;
- and
- establishing the parameter set of the target voice frame according to the parameters respectively corresponding to the plurality of second NNs;
- and reconstructing the target voice frame according to the parameter set.
A non-transitory computer-readable storage medium storing computer program instructions executable by at least one processor to perform:
- determining a historical voice frame corresponding to a target voice frame;
- determining a frequency-domain characteristic of the historical voice frame;
- invoking a network model to predict the frequency-domain characteristic of the historical voice frame, to obtain a parameter set of the target voice frame, the parameter set including a plurality of types of parameters, the network model including a first neural network (NN) and a plurality of second NNs, and invoking the network model comprising:
- invoking the first NN to predict the frequency-domain characteristic of the historical voice frame, to obtain a virtual frequency-domain characteristic of the target voice frame;
- invoking the second NNs to predict the virtual frequency-domain characteristic of the target voice frame, to obtain parameters corresponding to the second NNs;
- and
- establishing the parameter set of the target voice frame according to the parameters respectively corresponding to the plurality of second NNs;
- and reconstructing the target voice frame according to the parameter set.
Dependencies are inferred from the claim text.