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- W39776430 abstract "This paper introduces a general approach for binaryclassification of audiovisual data. The intended application ismispronunciation detection for specific phonemic errors, usingvery sparse training data. The system uses a Support VectorMachine (SVM) classifier with features obtained from a TimeVarying Discrete Cosine Transform (TV-DCT) on the audiolog-spectrum as well as on the image sequences. Theconcatenated feature vectors from both the modalities werereduced to a very small subset using a combination of featureselection methods. We achieved 95-100% correctclassification for each pair-wise classifier on a database ofSwedish vowels with an average of 58 instances per vowel fortraining. The performance was largely unaffected when testedon data from a speaker who was not included in the training." @default.
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- W39776430 date "2010-01-01" @default.
- W39776430 modified "2023-09-23" @default.
- W39776430 title "Detection of specific mispronunciations using audiovisual features." @default.
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