A language recognition method and system based on an adaptive central anchor
A language recognition and self-adaptive technology, applied in neural learning methods, natural language translation, special data processing applications, etc., can solve the problem of low accuracy and reliability of deep learning methods, improve accuracy and enhance feature expression capabilities. , the effect of improving the accuracy
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Embodiment 1
[0053] Such as figure 1 As shown, this embodiment discloses a language recognition method based on an adaptive central anchor, including the following steps:
[0054] S100: Construct a language data set, and preprocess the voice data in the language data set;
[0055] The language data set (vocal database) adopts the existing language database. The sampling rate of the language data set is 16000HZ, including 35 languages, and 10 hours of voice data are randomly selected for each language;
[0056] Taking one of the language datasets as an example, the speech data in the language dataset is preprocessed, including:
[0057] S110: Extract all the voice data of the same language in the language data set for splicing; splicing all the voices extracted from the language data set is recorded as S src ;
[0058] S120: Calculate the continuous silent segment in the speech data after splicing, if the continuous silent segment is greater than the set threshold, remove the silent part ...
Embodiment 2
[0135] Such as Image 6 As shown, this embodiment discloses a language recognition system based on an adaptive central anchor, including a language data set construction module, an enhancement processing module, a feature extraction module, a first training module, a second training module, and a language recognition module;
[0136] The language data set building block is used to construct the language data set;
[0137] The enhanced processing module is used to perform enhanced processing on the voice data in the language data set;
[0138] Feature extraction module is used for extracting the feature of the speech data after described enhancement processing, generates feature data set;
[0139] The first training module is used to construct a deep neural backbone network, and train the deep neural backbone network in a supervised learning manner based on a classification loss function;
[0140] The second training module is used to further train the deep neural backbone ne...
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