Paper Info
Title
A Deep Neural Network Based End To End Model For Joint Height And Age Estimation From Short Duration Speech
Abstract
Automatic height and age prediction of a speaker has a wide variety of applications in speaker profiling, forensics etc. Often in such applications only a few seconds of speech data is available to reliably estimate the speaker parameters. Traditionally, age and height were predicted separately using different estimation algorithms. In this work, we propose a unified DNN architecture to predict both height and age of a speaker for short durations of speech. A novel initialization scheme for the deep neural architecture is introduced, that avoids the requirement for a large training dataset. We evaluate the system on TIMIT dataset where the mean duration of speech segments is around 2.5s. The DNN system is able to improve the age RMSE by at least 0:6 years as compared to a conventional support vector regression system trained on Gaussian Mixture Model mean supervectors. The system achieves an RMSE error of 6:85 and 6:29 cm for male and female height prediction. In case of age estimation, the RMSE errors are 7:60 and 8:63 years for male and female respectively. Analysis of shorter speech segments reveals that even with 1 second speech input the performance degradation is at most 3 % compared to the full duration speech files.
Year
DOI
Venue
2019
10.1109/icassp.2019.8683397
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Keywords
Field
DocType
Automatic Joint Height and Age Estimation, Support Vector Regression, Deep neural network, Short duration
TIMIT,Pattern recognition,Computer science,Profiling (computer programming),End-to-end principle,Support vector machine,Mean squared error,Artificial intelligence,Initialization,Artificial neural network,Mixture model
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Shareef Babu Kalluri110.70
Deepu Vijayasenan27310.91
Sriram Ganapathy325239.62