1. A method, comprising: at a computing device that comprises at least one computer processor: receiving an acoustic signal at an automatic speech recognition (ASR) system, the ASR system configured to identify words in multiple different languages, the ASR system comprises a deep neural network (DNN), the DNN comprises an output layer that includes at least one softmax layer, the at least one softmax layer comprises output nodes, the output nodes correspond to the multiple different languages, wherein the DNN is trained based at least in part upon training data, the training data comprising spoken utterances in a source language, the acoustic signal comprising a spoken utterance that includes a word in a target language, the target language being different from the source language; extracting a plurality of features from the acoustic signal to form a feature vector; providing the feature vector to an input layer of the DNN, the DNN producing an output at the output layer responsive to being provided with the feature vector; identifying the word in the target language in the spoken utterance based upon the output of the DNN at the output layer; and performing at least one computing operation based upon the word in the target language in the spoken utterance being identified.
1. A method, comprising: at a computing device that comprises at least one computer processor: receiving an acoustic signal at an automatic speech recognition (ASR) system, the ASR system configured to identify words in multiple different languages, the ASR system comprises a deep neural network (DNN), the DNN comprises an output layer that includes at least one softmax layer, the at least one softmax layer comprises output nodes, the output nodes correspond to the multiple different languages, wherein the DNN is trained based at least in part upon training data, the training data comprising spoken utterances in a source language, the acoustic signal comprising a spoken utterance that includes a word in a target language, the target language being different from the source language; extracting a plurality of features from the acoustic signal to form a feature vector; providing the feature vector to an input layer of the DNN, the DNN producing an output at the output layer responsive to being provided with the feature vector; identifying the word in the target language in the spoken utterance based upon the output of the DNN at the output layer; and performing at least one computing operation based upon the word in the target language in the spoken utterance being identified. 9. The method of claim 1 , wherein the DNN comprises a plurality of hidden layers and the at least one softmax layer includes a single softmax layer, wherein supervised learning is employed to train the DNN to learn values of parameters of the hidden layers and the single softmax layers based upon the training data.