A method for training a voice data set, the method being performed in a computer device, and comprising: obtaining a first voice data set; separately selecting data from a first voice data set to generate a training set and a first test set; performing voice model training for the training set to obtain a preset quantity of first voice models; testing the first test set by using the preset quantity of first voice models separately to obtain at least one first voice model having a word error rate within a preset range; selecting the at least one first voice model as the optimal first voice model; and using a parameter of the at least one optimal first voice model having the word error rate within the preset range as the first voice model parameter; obtaining a second voice data set, and randomly selecting data from the second voice data set to generate a second test set; testing the second test set using the optimal first voice model; and on a condition that the second test set and the first test set satisfy a similarity condition, wherein the similarity condition is satisfied if a difference between a word error rate corresponding to the second test set and a minimum word error rate within the preset range is less than or equal to a tolerance threshold: performing second voice model training for the second voice data set by using second voice models and the first voice model parameter, wherein the first voice models are each a combination of a hidden Markov model, HMM, and a Gaussian mixture model, GMM and the second voice models are each a combination of a HMM and a deep neural network model, DNN, and wherein the first voice model parameter is defined as a duration of a state in the HMM. obtaining a first voice data set; separately selecting data from a first voice data set to generate a training set and a first test set; performing voice model training for the training set to obtain a preset quantity of first voice models; testing the first test set by using the preset quantity of first voice models separately to obtain at least one first voice model having a word error rate within a preset range; selecting the at least one first voice model as the optimal first voice model; and using a parameter of the at least one optimal first voice model having the word error rate within the preset range as the first voice model parameter; obtaining a second voice data set, and randomly selecting data from the second voice data set to generate a second test set; testing the second test set using the optimal first voice model; and on a condition that the second test set and the first test set satisfy a similarity condition, wherein the similarity condition is satisfied if a difference between a word error rate corresponding to the second test set and a minimum word error rate within the preset range is less than or equal to a tolerance threshold: performing second voice model training for the second voice data set by using second voice models and the first voice model parameter, wherein the first voice models are each a combination of a hidden Markov model, HMM, and a Gaussian mixture model, GMM and the second voice models are each a combination of a HMM and a deep neural network model, DNN, and wherein the first voice model parameter is defined as a duration of a state in the HMM.