Artificial intelligence-based acoustic model training method and apparatus, device and storage medium
AssigneeBaidu Online Network Technology (Beijing) Co., Ltd.
InventorsBin HUANG, Yiping PENG
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Abstract
The present disclosure provides an artificial intelligence-based acoustic model training method and apparatus, a device and a storage medium, wherein the method comprises: obtaining manually-annotated speech data; training according to the manually-annotated speech data to obtain a first acoustic model; obtaining unannotated speech data; training according to the unannotated speech data and the first acoustic model to obtain a desired second acoustic model. The solution of the present disclosure can be applied to save manpower costs and improve the training efficiency.
Claims21
An artificial intelligence-based acoustic model training method, wherein the method comprises:
- obtaining manually-annotated speech data;
- training according to the manually-annotated speech data to obtain a first acoustic model;
- obtaining unannotated speech data;
- training according to the unannotated speech data and the first acoustic model to obtain a desired second acoustic model.
A computer device, comprising a memory, a processor and a computer program which is stored on the memory and runs on the processor, wherein the processor, upon executing the program, implements the following operation:
- obtaining manually-annotated speech data;
- training according to the manually-annotated speech data to obtain a first acoustic model;
- obtaining unannotated speech data;
- training according to the unannotated speech data and the first acoustic model to obtain a desired second acoustic model.
A computer-readable storage medium on which a computer program is stored, wherein the program, when executed by a processor, implements the following operation:
- obtaining manually-annotated speech data;
- training according to the manually-annotated speech data to obtain a first acoustic model;
- obtaining unannotated speech data;
- training according to the unannotated speech data and the first acoustic model to obtain a desired second acoustic model.
Dependencies are inferred from the claim text.