A computer-implemented speech recognition method for on-line speaker adaptive recognition, the method comprising: extracting from speech data, via a first neural network configured to classify speakers according to speech data received by an input layer of the first neural network, a vector containing speaker recognition features, wherein the vector containing speaker recognition features is an output of a last hidden layer of the first neural network; compensating bias in a second neural network in accordance with the vector containing the speaker recognition features by: determining a bias term for the second neural network configured to recognize speech according to input speech data, wherein determining the bias term includes multiplying the extracted vector containing speaker recognition features by a weight matrix to generate the bias term, and transmitting the bias term to bias nodes corresponding to one or more layers (232, 233), except for an input layer, in the second neural network; and recognizing speech in the speech data, via an acoustic model based on the second neural network, the speech data received by the input layer of the second neural network, wherein the speech data received by the input layer of the first neural network and the speech data received by the input layer of the second neural network are the same speech data. extracting from speech data, via a first neural network configured to classify speakers according to speech data received by an input layer of the first neural network, a vector containing speaker recognition features, wherein the vector containing speaker recognition features is an output of a last hidden layer of the first neural network; compensating bias in a second neural network in accordance with the vector containing the speaker recognition features by: determining a bias term for the second neural network configured to recognize speech according to input speech data, wherein determining the bias term includes multiplying the extracted vector containing speaker recognition features by a weight matrix to generate the bias term, and transmitting the bias term to bias nodes corresponding to one or more layers (232, 233), except for an input layer, in the second neural network; and recognizing speech in the speech data, via an acoustic model based on the second neural network, the speech data received by the input layer of the second neural network, wherein the speech data received by the input layer of the first neural network and the speech data received by the input layer of the second neural network are the same speech data. determining a bias term for the second neural network configured to recognize speech according to input speech data, wherein determining the bias term includes multiplying the extracted vector containing speaker recognition features by a weight matrix to generate the bias term, and transmitting the bias term to bias nodes corresponding to one or more layers (232, 233), except for an input layer, in the second neural network; and recognizing speech in the speech data, via an acoustic model based on the second neural network, the speech data received by the input layer of the second neural network, wherein the speech data received by the input layer of the first neural network and the speech data received by the input layer of the second neural network are the same speech data.