Abstract: A system for end-to-end automated scoring is disclosed. The system includes a word embedding layer for converting a plurality of ASR outputs into input tensors; a neural network lexical model encoder receiving the input tensors; a neural network acoustic model encoder implementing AM posterior probability, word duration, mean value of pitch and mean value of intensity based on a plurality of cues; and a linear regression module, for receiving concatenated encoded features from the neural network lexical model encoder and the neural network acoustic model encoder.

Abstract: Provide automatic assessment of oral recitations during computer based language assessments using a trained neural network to automate the scoring and feedback processes without human transcription and scoring input by automatically generating a score of a language assessment. Providing an automatic speech recognition (“ASR”) scoring system. Training multiple scoring reference vectors associated with multiple possible scores of an assessment, and receiving an acoustic language assessment response to an assessment item. Based on the acoustic language assessment automatically generating a transcription, and generating an individual word vector from the transcription. Generating an input vector by concatenating an individual word vector with a transcription feature vector, and supplying an input vector as input to a neural network. Generating an output vector based on weights of a neural network; and generating a score by comparing an output vector with scoring vectors.

Abstract: Systems and methods are provided for a processor-implemented method of analyzing quality of sound acquired via a microphone. An input metric is extracted from a sound recording at each of a plurality of time intervals. The input metric is provided at each of the time intervals to a neural network that includes a memory component, where the neural network provides an output metric at each of the time intervals, where the output metric at a particular time interval is based on the input metric at a plurality of time intervals other than the particular time interval using the memory component of the neural network. The output metric is aggregated from each of the time intervals to generate a score indicative of the quality of the sound acquired via the microphone.

Abstract: Systems and methods are provided for providing voice authentication of a candidate speaker. Training data sets are accessed, where each training data set comprises data associated with a training speech sample of a speaker and a plurality of speaker metrics, where the plurality of speaker metrics include a native language of the speaker. The training data sets are used to train a neural network, where the data associated with each training speech sample is a training input to the neural network, and each of the plurality of speaker metrics is a training output to the neural network. Data associated with a speech sample is provided to the neural network to generate a vector that contains values for the plurality of speaker metrics, and the values contained in the vector are compared to values contained in a reference vector associated with a known person to determine whether the candidate speaker is the known person.

Abstract: Provide automatic assessment of oral recitations during computer based language assessments using a trained neural network to automate the scoring and feedback processes without human transcription and scoring input by automatically generating a score of a language assessment. Providing an automatic speech recognition (“ASR”) scoring system. Training multiple scoring reference vectors associated with multiple possible scores of an assessment, and receiving an acoustic language assessment response to an assessment item. Based on the acoustic language assessment automatically generating a transcription, and generating an individual word vector from the transcription. Generating an input vector by concatenating an individual word vector with a transcription feature vector, and supplying an input vector as input to a neural network. Generating an output vector based on weights of a neural network; and generating a score by comparing an output vector with scoring vectors.