Research on LSTM+Attention Model of Infant Cry Classification

DOI

10.2991/jrnal.k.210922.013How to use a DOI?

Keywords

Children’s emotional; time–sequence relationship; frame-level speech feature; deep attention gate; long- and short-term memory (LSTM)

Abstract

According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and the infant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established. The frame-level speech features are used instead of the traditional statistical features to preserve the temporal relationships in the original speech, and the traditional forgetting and input gates are transformed into attention gates by introducing an attention mechanism, to improve the performance of speech emotion recognition, the depth attention gate is calculated according to the self-defined depth strategy. The results show that, in Fau Aibo Children’s emotional data corpus and baby crying emotional needs database, compared with the traditional LSTM based model, the recall rate and F1-score of this model are 3.14%, 5.50%, 1.84% and 5.49% higher, respectively, compared with the traditional model based on LSTM and gated recurrent unit, the training time is shorter and the speech emotion recognition rate of baby is higher.

Copyright

© 2021 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

TY  - JOUR
AU  - Tianye Jian
AU  - Yizhun Peng
AU  - Wanlong Peng
AU  - Zhou Yang
PY  - 2021
DA  - 2021/10/09
TI  - Research on LSTM+Attention Model of Infant Cry Classification
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 218
EP  - 223
VL  - 8
IS  - 3
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.210922.013
DO  - 10.2991/jrnal.k.210922.013
ID  - Jian2021
ER  -