Age Estimation in Short Speech Utterances Based on LSTM Recurrent Neural Networks

Age estimation from speech has recently received increased interest as it is useful for many applications such as user-profiling, targeted marketing, or personalized call-routing. This kind of applications need to quickly estimate the age of the speaker and might greatly benefit from real-time capab...

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Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.22524-22530
Main Authors: Zazo, Ruben, Sankar Nidadavolu, Phani, Chen, Nanxin, Gonzalez-Rodriguez, Joaquin, Dehak, Najim
Format: Article
Language:English
Subjects:
Age
Automatic age estimation
Chronology
DNN
Estimation
Logic gates
LSTM
Neural networks
NIST
Real time
Real-time systems
Recurrent neural networks
RNN
Speech
Speech recognition
Time response
Voice activity detectors
Voice recognition
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Summary:Age estimation from speech has recently received increased interest as it is useful for many applications such as user-profiling, targeted marketing, or personalized call-routing. This kind of applications need to quickly estimate the age of the speaker and might greatly benefit from real-time capabilities. Long short-term memory (LSTM) recurrent neural networks (RNN) have shown to outperform state-of-the-art approaches in related speech-based tasks, such as language identification or voice activity detection, especially when an accurate real-time response is required. In this paper, we propose a novel age estimation system based on LSTM-RNNs. This system is able to deal with short utterances (from 3 to 10 s) and it can be easily deployed in a real-time architecture. The proposed system has been tested and compared with a state-of-the-art i-vector approach using data from NIST speaker recognition evaluation 2008 and 2010 data sets. Experiments on short duration utterances show a relative improvement up to 28% in terms of mean absolute error of this new approach over the baseline system.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2816163