Mandarin and dialect mixed modeling and speech recognition

A computer device, comprising a processor and a memory,

• wherein, the processor is configured to run a program corresponding to executable program codes by reading the executable program codes stored in the memory, to implement a modeling method for speech recognition, comprising:
• processing first speech data of Mandarin and first speech data of P dialects respectively based on a pre-trained alignment model to obtain a tag corresponding to each frame of the first speech data, and counting the obtained tags and performing deduplication on tags of each type to determine N types of tags, N being a positive integer and P being a positive integer;
• training a neural network according to second speech data of Mandarin, and generating a recognition model when the neural network converges, wherein outputs of the recognition model are the N types of tags;
• inputting second speech data of the P dialects into the recognition model for processing respectively to obtain an output tag of each frame of the second speech data of each dialect;
• for each dialect i of the P dialects, determining, according to the output tags and tagged true tags of the second speech data of each dialect, an error rate of each type of the N types of tags, wherein the error rate of a specific type of tag is a ratio of a total number of misclassifications for the specific type of tag to a total number of the specific type of tag in the tagged true tags;
• for each dialect i of the P dialects, generating, according to Mi types of tags whose error rates are greater than a preset threshold, Mi types of target tags specially used for the dialect i, where Mi being an integer greater than or equal to zero, and 0=<i<=P−1;
• and
• training an acoustic model according to third speech data of Mandarin and third speech data of the P dialects, wherein outputs of the acoustic model are the N types of tags and the Mi types of target tags corresponding to each dialect I of the P dialects.

A non-transitory computer readable storage medium having a computer program stored thereon that, when the program is executed by a processor, a modeling method for speech recognition, comprising:

• processing first speech data of Mandarin and first speech data of P dialects respectively based on a pre-trained alignment model to obtain a tag corresponding to each frame of the first speech data, and counting the obtained tags and performing deduplication on tags of each type to determine N types of tags, N being a positive integer and P being a positive integer;
• training a neural network according to second speech data of Mandarin, and generating a recognition model when the neural network converges, wherein outputs of the recognition model are the N types of tags;
• inputting second speech data of the P dialects into the recognition model for processing respectively to obtain an output tag of each frame of the second speech data of each dialect;
• for each dialect I of the P dialects, determining, according to the output tags and tagged true tags of the second speech data of each dialect, an error rate of each type of the N types of tags, wherein the error rate of a specific type of tag is a ratio of a total number of misclassifications for the specific type of tag to a total number of the specific type of tag in the tagged true tags;
• for each dialect i of the P dialects, generating, according to Mi types of tags whose error rates are greater than a preset threshold, Mi types of target tags specially used for the dialect i, where Mi being an integer greater than or equal to zero, and 0=<i<=P−1;
• and
• training an acoustic model according to third speech data of Mandarin and third speech data of the P dialects, wherein outputs of the acoustic model are the N types of tags and the Mi types of target tags corresponding to each dialect i of the P dialects.