11. A computing device comprising: at least one processor; and memory that comprises: a recognition system that is configured to detect words in multiple languages, the recognition system comprising: a deep neural network (DNN) that comprises: an input layer; a plurality of hidden layers, each hidden layer comprising a respective plurality of nodes, each node in a hidden layer being configured to perform a linear or nonlinear transformation on output of at least one node from an adjacent layer in the DNN, the plurality of hidden layers having parameters corresponding thereto, wherein values of the parameters are based upon training data that comprises acoustic signals that include spoken utterances in a plurality of different source languages; and at least one softmax layer that comprises modeling units that are representative of phonetic elements used in the multiple languages, the multiple languages include a target language, the at least one softmax layer having parameters corresponding thereto, wherein values of the parameters of the at least one softmax layer are based upon training data that comprises acoustic signals that include spoken utterances in the target language, the at least one softmax layer receiving outputs of nodes from an uppermost hidden layer in the DNN, wherein output of the at least one softmax layer is a probability distribution over the modeling units; and instructions that, when executed by the at least one processor, cause the at least one processor to perform acts comprising: receiving an acoustic signal that comprises a word in the target language; extracting features from the acoustic signal to generate a feature vector; providing the feature vector to the DNN; and identifying the word in the target language based upon the probability distribution over at least a subset of the modeling units.
11. A computing device comprising: at least one processor; and memory that comprises: a recognition system that is configured to detect words in multiple languages, the recognition system comprising: a deep neural network (DNN) that comprises: an input layer; a plurality of hidden layers, each hidden layer comprising a respective plurality of nodes, each node in a hidden layer being configured to perform a linear or nonlinear transformation on output of at least one node from an adjacent layer in the DNN, the plurality of hidden layers having parameters corresponding thereto, wherein values of the parameters are based upon training data that comprises acoustic signals that include spoken utterances in a plurality of different source languages; and at least one softmax layer that comprises modeling units that are representative of phonetic elements used in the multiple languages, the multiple languages include a target language, the at least one softmax layer having parameters corresponding thereto, wherein values of the parameters of the at least one softmax layer are based upon training data that comprises acoustic signals that include spoken utterances in the target language, the at least one softmax layer receiving outputs of nodes from an uppermost hidden layer in the DNN, wherein output of the at least one softmax layer is a probability distribution over the modeling units; and instructions that, when executed by the at least one processor, cause the at least one processor to perform acts comprising: receiving an acoustic signal that comprises a word in the target language; extracting features from the acoustic signal to generate a feature vector; providing the feature vector to the DNN; and identifying the word in the target language based upon the probability distribution over at least a subset of the modeling units. 16. The computing device of claim 11 , wherein the training data comprises acoustic signals that include spoken utterances in a plurality of different languages, and wherein the values of the parameters of the hidden layers are based upon the acoustic signals that include spoken utterances in the plurality of different languages.