Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition
Abstract
Speech emotion recognition plays a crucial role in human-computer interactions. However, most speech emotion recognition research is biased toward English-speaking adults, which hinders its applicability to other demographic groups in different languages and age groups. In this work, we analyze the transferability of emotion recognition across three different languages--English, Mandarin Chinese, and Cantonese; and 2 different age groups--adults and the elderly. To conduct the experiment, we develop an English-Mandarin speech emotion benchmark for adults and the elderly, BiMotion, and a Cantonese speech emotion dataset, YueMotion. This study concludes that different language and age groups require specific speech features, thus making cross-lingual inference an unsuitable method. However, cross-group data augmentation is still beneficial to regularize the model, with linguistic distance being a significant influence on cross-lingual transferability. We release publicly release our code at https://github.com/HLTCHKUST/elderly_ser.
Keywords
Cite
@article{arxiv.2306.14517,
title = {Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition},
author = {Samuel Cahyawijaya and Holy Lovenia and Willy Chung and Rita Frieske and Zihan Liu and Pascale Fung},
journal= {arXiv preprint arXiv:2306.14517},
year = {2023}
}
Comments
Accepted in INTERSPEECH 2023