A method, comprising: receiving an acoustic signal at an automatic speech recognition, ASR, system, the ASR system configured to identify words in a plurality of different languages, the ASR system comprising a deep neural network, DNN, (106, 300; 400), the DNN trained based at least in part upon training data, the training data comprising spoken utterances in a source language and in a target language, the acoustic signal comprising a spoken utterance that includes a word in the target language; extracting a plurality of features from the acoustic signal to form a feature vector; providing the feature vector to an input layer (108, 302) of the DNN (106, 300; 400), the DNN producing an output responsive to being provided with the feature vector; and identifying the word in the target language in the spoken utterance based upon the output of the DNN (106, 300; 400); wherein the DNN (106, 300; 400) comprises: a plurality of hidden layers (110, 312-318), wherein each hidden layer in the plurality of hidden layers comprises a respective plurality of nodes (320-326, 328-334, 336-342, 344-350), each node configured to perform a linear or nonlinear transformation on its respective input; a softmax layer (112, 352; 402) that receives outputs of respective nodes in an uppermost layer of the plurality of hidden layers, the softmax layer comprises a plurality of modeling units (356-362; 404-410) that are representative of respective senones used in the target language; and characterized in that the DNN (106, 300; 400) comprises a plurality of softmax layers (112, 352, 354; 402), each softmax layer corresponding to a respective target language. receiving an acoustic signal at an automatic speech recognition, ASR, system, the ASR system configured to identify words in a plurality of different languages, the ASR system comprising a deep neural network, DNN, (106, 300; 400), the DNN trained based at least in part upon training data, the training data comprising spoken utterances in a source language and in a target language, the acoustic signal comprising a spoken utterance that includes a word in the target language; extracting a plurality of features from the acoustic signal to form a feature vector; providing the feature vector to an input layer (108, 302) of the DNN (106, 300; 400), the DNN producing an output responsive to being provided with the feature vector; and identifying the word in the target language in the spoken utterance based upon the output of the DNN (106, 300; 400); wherein the DNN (106, 300; 400) comprises: a plurality of hidden layers (110, 312-318), wherein each hidden layer in the plurality of hidden layers comprises a respective plurality of nodes (320-326, 328-334, 336-342, 344-350), each node configured to perform a linear or nonlinear transformation on its respective input; a softmax layer (112, 352; 402) that receives outputs of respective nodes in an uppermost layer of the plurality of hidden layers, the softmax layer comprises a plurality of modeling units (356-362; 404-410) that are representative of respective senones used in the target language; and characterized in that the DNN (106, 300; 400) comprises a plurality of softmax layers (112, 352, 354; 402), each softmax layer corresponding to a respective target language. a plurality of hidden layers (110, 312-318), wherein each hidden layer in the plurality of hidden layers comprises a respective plurality of nodes (320-326, 328-334, 336-342, 344-350), each node configured to perform a linear or nonlinear transformation on its respective input; a softmax layer (112, 352; 402) that receives outputs of respective nodes in an uppermost layer of the plurality of hidden layers, the softmax layer comprises a plurality of modeling units (356-362; 404-410) that are representative of respective senones used in the target language; and characterized in that the DNN (106, 300; 400) comprises a plurality of softmax layers (112, 352, 354; 402), each softmax layer corresponding to a respective target language.