Patent application number | Description | Published |
20150134917 | REMOTE MATERIALIZATION OF LOW VELOCITY DATA - A system includes reception of a client query identifying data stored by a remote data source, generation of a remote query of the remote data source based on the client query, determination of a cache name based on the remote query, determination of whether the remote data source comprises a cache associated with the cache name and, if it is determined that the remote data source comprises a valid cache associated with the cache name, instruction of the remote data source to read the data of the cache, and reception of the data of the cache from the remote data source. | 05-14-2015 |
20160125056 | VIRTUAL FUNCTION AS QUERY OPERATOR - A system includes definition of a remote data source, definition of a virtual function specifying executable job code, a return data format and a data location in the remote data source, reception of a structured language query including the virtual function as a data source, and, in response to the received query, instruction of the remote data source to execute the job code based on data in the data location and return data in the return data format. | 05-05-2016 |
Patent application number | Description | Published |
20130262110 | Unsupervised Language Model Adaptation for Automated Speech Scoring - Systems and methods are provided for generating a transcript of a speech sample response to a test question. The speech sample response to the test question is provided to a language model, where the language model is configured to perform an automated speech recognition function. The language model is adapted to the test question to improve the automated speech recognition function by providing to the language model automated speech recognition data related to the test question, Internet data related to the test question, or human-generated transcript data related to the test question. The transcript of the speech sample is generated using the adapted language model. | 10-03-2013 |
20140255886 | Systems and Methods for Content Scoring of Spoken Responses - Computer-implemented systems and methods are provided for automatically scoring the content of moderately predictable responses. For example, a computer performing the content scoring analysis can receive a response (either in text or spoken form) to a prompt. The computer can determine the content correctness of the response by analyzing one or more content features. One of the content features is analyzed by applying one or more regular expressions, determined based on training responses associated with the prompt. Another content feature is analyzed by applying one or more context free grammars, determined based on training responses associated with the prompt. Another content feature is analyzed by applying a keyword list, determined based on the test prompt eliciting the response and/or stimulus material. Another content feature is analyzed by applying one or more probabilistic n-gram models, determined based on training responses associated with the prompt. Another content feature is analyzed by comparing a POS response vector, determined based on the response, to one or more POS training vectors, determined based on training responses associated with the prompt. Another content feature is analyzed by comparing a response n-gram count to one or more training n-gram counts using an n-gram matching evaluation metric (e.g., BLEU). Another content feature is analyzed by comparing the response to one to training responses associated with the prompt using a dissimilarity metric (e.g., edit distance and word error rate). | 09-11-2014 |
20150194147 | Non-Scorable Response Filters for Speech Scoring Systems - A method for scoring non-native speech includes receiving a speech sample spoken by a non-native speaker and performing automatic speech recognition and metric extraction on the speech sample to generate a transcript of the speech sample and a speech metric associated with the speech sample. The method further includes determining whether the speech sample is scorable or non-scorable based upon the transcript and speech metric, where the determination is based on an audio quality of the speech sample, an amount of speech of the speech sample, a degree to which the speech sample is off-topic, whether the speech sample includes speech from an incorrect language, or whether the speech sample includes plagiarized material. When the sample is determined to be non-scorable, an indication of non-scorability is associated with the speech sample. When the sample is determined to be scorable, the sample is provided to a scoring model for scoring. | 07-09-2015 |