5. The method of claim 4 , wherein: generating the plurality of triphones by linking phonemes in the phoneme collection corresponding to the language includes, for each phoneme in the phoneme collection: obtaining a context phoneme; and generating a triphone by linking the context phoneme to a corresponding monophone for the phoneme; performing Gaussian splitting training on the triphones that are clustered with the decision tree corresponding to the language updates a parameter of the clustered triphone; and training the foreground model further includes: for each phoneme in the characteristic phoneme collection: training an initial hidden Markov model (HMM) for three statuses of a respective phoneme in the characteristic phoneme collection; obtaining data related to the respective phoneme from the collected labeled data; updating the initial HMM with the obtained data so as to obtain a monophone model for the respective phoneme; and after performing the Gaussian splitting training on the triphones that are clustered with a decision tree corresponding to the language, performing minimum phoneme error discriminative training so as to obtain triphone models for respective phonemes in the phoneme collection corresponding to the language; and training the foreground model based on the obtained monophone and triphone models.
5. The method of claim 4 , wherein: generating the plurality of triphones by linking phonemes in the phoneme collection corresponding to the language includes, for each phoneme in the phoneme collection: obtaining a context phoneme; and generating a triphone by linking the context phoneme to a corresponding monophone for the phoneme; performing Gaussian splitting training on the triphones that are clustered with the decision tree corresponding to the language updates a parameter of the clustered triphone; and training the foreground model further includes: for each phoneme in the characteristic phoneme collection: training an initial hidden Markov model (HMM) for three statuses of a respective phoneme in the characteristic phoneme collection; obtaining data related to the respective phoneme from the collected labeled data; updating the initial HMM with the obtained data so as to obtain a monophone model for the respective phoneme; and after performing the Gaussian splitting training on the triphones that are clustered with a decision tree corresponding to the language, performing minimum phoneme error discriminative training so as to obtain triphone models for respective phonemes in the phoneme collection corresponding to the language; and training the foreground model based on the obtained monophone and triphone models. 6. The method of claim 5 , further including: calculating a Gaussian Mixed Model (GMM) distance between two monophone models; comparing the calculated GMM distance with a predefined similarity threshold value; and in accordance with a determination that the calculated GMM distance is larger than the predefined similarity threshold value: clustering the two monophones corresponding to the two monophone models; and recording the two monophones in a confusion matrix, wherein the confusion matrix is configured to describe similar monophones.