conftrace_
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.

🚀 Conference Pioneer - INTERSPEECH 2016
🌉 Interdisciplinary Bridge - Machine Learning and Speech & Audio
🧭 Keyword Pioneer - speaker age
🐣 Hot Topic Early Bird - feature fusion
🐝 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