End-to-End Speaker Height and age estimation using Attention Mechanism with LSTM-RNN

01/13/2021
by   Manav Kaushik, et al.
0

Automatic height and age estimation of speakers using acoustic features is widely used for the purpose of human-computer interaction, forensics, etc. In this work, we propose a novel approach of using attention mechanism to build an end-to-end architecture for height and age estimation. The attention mechanism is combined with Long Short-Term Memory(LSTM) encoder which is able to capture long-term dependencies in the input acoustic features. We modify the conventionally used Attention – which calculates context vectors the sum of attention only across timeframes – by introducing a modified context vector which takes into account total attention across encoder units as well, giving us a new cross-attention mechanism. Apart from this, we also investigate a multi-task learning approach for jointly estimating speaker height and age. We train and test our model on the TIMIT corpus. Our model outperforms several approaches in the literature. We achieve a root mean square error (RMSE) of 6.92cm and6.34cm for male and female heights respectively and RMSE of 7.85years and 8.75years for male and females ages respectively. By tracking the attention weights allocated to different phones, we find that Vowel phones are most important whistlestop phones are least important for the estimation task.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/24/2021

Learning Speaker Representation with Semi-supervised Learning approach for Speaker Profiling

Speaker profiling, which aims to estimate speaker characteristics such a...
research
01/07/2021

Attention-based multi-task learning for speech-enhancement and speaker-identification in multi-speaker dialogue scenario

Multi-task learning (MTL) and attention mechanism have been proven to ef...
research
03/22/2017

Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection

Traditional speaker change detection in dialogues is typically based on ...
research
03/31/2019

Learning Shared Encoding Representation for End-to-End Speech Recognition Models

In this work, we learn a shared encoding representation for a multi-task...
research
05/31/2021

Multi-Scale Attention Neural Network for Acoustic Echo Cancellation

Acoustic Echo Cancellation (AEC) plays a key role in speech interaction ...
research
10/08/2021

Explaining the Attention Mechanism of End-to-End Speech Recognition Using Decision Trees

The attention mechanism has largely improved the performance of end-to-e...

Please sign up or login with your details

Forgot password? Click here to reset