Abstract: Systems and methods are disclosed for automatically scoring a constructed response using a neural network. In embodiments, a constructed response received by a processing system may be processed to divide the constructed response into multiple series of word tokens, wherein each word token includes a sequence of characters. The constructed response may be further processed to correct one or more spelling errors. The word tokens may be encoded to generate representation vectors for the constructed response. A set of nonlinear operations may be applied to the plurality of representation vectors in a neural network to generate a single vector output. A set of predetermined network weights may be applied to the vector output of the neural network to generate a scalar output for scoring the constructed response.

Abstract: Systems and methods for identifying a person's native language, are presented. A native language identification system, comprising a plurality of artificial neural networks, such as time delay deep neural networks, is provided. Respective artificial neural networks of the plurality of artificial neural networks are trained as universal background models, using separate native language and non-native language corpora. The artificial neural networks may be used to perform voice activity detection and to extract sufficient statistics from the respective language corpora. The artificial neural networks may use the sufficient statistics to estimate respective T-matrices, which may in turn be used to extract respective i-vectors. The artificial neural networks may use i-vectors to generate a multilayer perceptron model, which may be used to identify a person's native language, based on an utterance by the person in his or her non-native language.