2016
INTERSPEECH
INTERSPEECH 2016
Speaker Age Classification and Regression Using i-Vectors
Abstract
In this paper, we examine the use of i-vectors both for age regression as well as for age classification. Although i-vectors have been previously used for age regression task, we extend this approach by applying fusion of i-vectors and acoustic features regression to estimate the speaker age. By our fusion we obtain a relative improvement of 12.6% comparing to solely i-vector system. We also use i-vectors for age classification, which to our knowledge is the first attempt to do so. Our best results reach unweighted accuracy 62.9%, which is a relative improvement of 16.7% comparing to the best results obtained in age classification task at Age Sub-Challenge at Interspeech 2010.
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Conference Pioneer
— INTERSPEECH 2016
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Interdisciplinary Bridge
— Machine Learning and Speech & Audio
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Keyword Pioneer
— speaker age
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Hot Topic Early Bird
— feature fusion
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio