Phone-level pronunciation scoring and assessment for interactive language learning
Speech Communication
Combination of machine scores for automatic grading of pronunciation quality
Speech Communication
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
An overview of spoken language technology for education
Speech Communication
Automatic scoring of non-native spontaneous speech in tests of spoken English
Speech Communication
Corpus-based and knowledge-based measures of text semantic similarity
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Improved pronunciation features for construct-driven assessment of non-native spontaneous speech
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Towards automatic scoring of a test of spoken language with heterogeneous task types
EANL '08 Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Vocabulary profile as a measure of vocabulary sophistication
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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Most previous research on automated speech scoring has focused on restricted, predictable speech. For automated scoring of unrestricted spontaneous speech, speech proficiency has been evaluated primarily on aspects of pronunciation, fluency, vocabulary and language usage but not on aspects of content and topicality. In this paper, we explore features representing the accuracy of the content of a spoken response. Content features are generated using three similarity measures, including a lexical matching method (Vector Space Model) and two semantic similarity measures (Latent Semantic Analysis and Pointwise Mutual Information). All of the features exhibit moderately high correlations with human proficiency scores on human speech transcriptions. The correlations decrease somewhat due to recognition errors when evaluated on the output of an automatic speech recognition system; however, the additional use of word confidence scores can achieve correlations at a similar level as for human transcriptions.