[PDF] Top 20 Exploring Content Features for Automated Speech Scoring

... score between two documents is then calculated by combining the similarity of the words they contain, weighted by their word specificity (i.e., IDF values). In this paper, we use these three similarity mea- sures to ... See full document

... some automated spoken language assessment systems have also in- cluded tasks which elicit spontaneous ...their scoring models (Zechner et al., 2009), since these types of features are relatively ... See full document

... Since many responses in ETLA are expected to follow certain patterns, it is intuitive to construct limited regular expressions (RegEx) to match gold standard responses for candidates with high profi- ciency score levels. ... See full document

... linguistic features, for auto- matically scoring spoken picture-based narration ...collocational features: the maximum, minimum and the median MI, and the propor- tion of bigrams’ and trigrams’ MI ... See full document

... the speech files are transcribed by both human and ASR, same experiments are run on both data sets to compare representation performance on differ- ent ...tent features to speaking proficiency are computed ... See full document

... an automated Japanese essay scoring system called ...essay features compared with many professional writings for each prompt, our system can evaluate ...three features are exam- ined: (1) ... See full document

... The average of the human scores was 2.58, and the most frequent score was 3 (48%), followed by 2 (39%), 4 (8%), 1 (4%), and 0 (1%). The number of words in the transcriptions generated by an automated speech ... See full document

... make automated speech scoring difficult. When automated scoring is used in the context of a high stakes language proficiency as- sessment, for which the scores are used to make ... See full document

... The low performance of the c-rater models for the unseen form was somewhat expected. The models learned characteristic n-grams of specific Key Points from the training data. The Key Points in this study were largely ... See full document

... and speech processing have given rise to the research and development of automated speech scoring systems in the past 10-15 ...low-level features, such as pronun- ciation ...(e.g. ... See full document

... an automated scoring engine orig- inally designed to evaluate the content of a student ...n-gram features, the models also include syntactic dependency ...sparse features, the modeling ... See full document

... using speech processing and natural language pro- cessing (NLP) technologies to automatically score speaking tests (Eskenazi, ...of features related to speech delivery, such as fluency, pronun- ... See full document

... We also performed manual error analysis on a small set of highly discrepant machine and hu- man scores and found that a substantial subset of the data investigated had human rater errors that caused score discrepancies ... See full document

... It is important to note that the grading guide- lines for the national exams do not focus exclu- sively on the quality of the language used, but rather on the ability of the student to produce a coher- ent and convincing ... See full document

... incorporating features into the discriminative MaxEnt classifier motivate the model choice for the problem at ...(correlated) features makes it directly applicable to address the first lim- itation of the ... See full document

... CMV features and the SMR vectors for the other two sets(could be either CRR sets or ASR ...six features of each set can be ex- tracted from the FST ... See full document

... the content similarity between ...two content features based on this similarity ...final scoring model, using both content features, as well as features related to other ... See full document

... We consider several methods of automatic fea- ture selection commonly applied to linear models (Hastie et al., 2013). These include subset selec- tion methods such as step-wise feature selection as well as shrinkage ... See full document

... adding content features lead to a statistically significant improvement in model performance for Integrated questions, this improvement was small and the scores from the two models were highly cor- related ... See full document

... complexity features, based on clausal or parse tree information derived from human transcriptions of spoken test responses, can predict holistic human rater scores for combined speaker responses over a whole test ... See full document