Systems and Methods for Natural Language Processing for Speech Content Scoring

摘要

Computer-implemented systems and methods are provided for scoring content of a spoken response to a prompt. A scoring model is generated for a prompt, where generating the scoring model includes generating a transcript for each of a plurality of training responses to the prompt, dividing the plurality of training responses into clusters based on the transcripts of the training responses, selecting a subset of the training responses in each cluster for scoring, scoring the selected subset of training responses for each cluster, and generating content training vectors using the transcripts from the scored subset. A transcript is generated for a received spoken response to be scored, and a similarity metric is computed between the transcript of the spoken response to be scored and the content training vectors. A score is assigned to the spoken response based on the determined similarity metric.

主权项

1. A computer-implemented method of scoring content of a spoken response to a prompt, comprising:
generating a scoring model for a prompt, wherein generating the scoring model comprises:
generating a transcript for each of a plurality of training responses to the prompt;dividing the plurality of training responses into clusters based on the transcripts of the training responses;selecting a subset of the training responses in each cluster for scoring;scoring the selected subset of training responses for each cluster; andgenerating content training vectors using the transcripts from the scored subset; generating transcript for a received spoken response to be scored; computing a similarity metric between the transcript of the spoken response to be scored and the content training vectors; and assigning a score to the spoken response based on the similarity metric.