Drink and Speak: On the automatic classification of alcohol intoxination by acoustic, prosodic and text-based features
- This paper focuses on the automatic detection of a person's blood level alcohol based on automatic speech processing approaches. We compare 5 different feature types with different ways of modeling. Experiments are based on the ALC corpus of IS2011 Speaker State Challenge. The classification task is restricted to the detection of a blood alcohol level above 0.5‰. Three feature sets are based on spectral observations: MFCCs, PLPs, TRAPS. These are modeled by GMMs. Classification is either done by a Gaussian classifier or by SVMs. In the later case classification is based on GMM-based supervectors, i.e. concatenation of GMM mean vectors. A prosodic system extracts a 292-dimensional feature vector based on a voiced-unvoiced decision. A transcription-based system makes use of text transcriptions related to phoneme durations and textual structure. We compare the stand-alone performances of these systems and combine them on score level by logistic regression. The best stand-alone performance is the transcriptionbased system which outperforms the baseline by 4.8% on the development set. A Combination on score level gave a huge boost when the spectral-based systems were added (73.6%). This is a relative improvement of 12.7% to the baseline. On the test-set we achieved an UA of 68.6% which is a significant improvement of 4.1% to the baseline system.
MetadatenAuthor: | Tobias Bocklet, Korbinian Riedhammer, Elmar Nöth |
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Parent Title (English): | INTERSPEECH 2011, 12th Annual Conference of the International Speech Communication Association, Florence, Italy, August 2011. |
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Document Type: | Conference Proceeding |
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Language: | English |
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Publication Year: | 2011 |
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Tag: | GMM; alcohol intoxication; system fusion |
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First Page: | 3213 |
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Last Page: | 3216 |
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faculties / departments: | Fakultät für Informatik |
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Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
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