Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition

Samuel Cahyawijaya, Holy Lovenia, Willy Chung, Rita Frieske, Zihan Liu +1 more
6/26/2023
cs.CLcs.SDeess.AS

Official code: HLTCHKUST/elderly_ser (21 stars, MIT, last commit 2023-06-30).

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.

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Code Implementations(1)

HLTCHKUST/elderly_serOfficial100%
MIT

Transferability of cross-lingual and cross-age speech emotion recognition

213Shell, PythonJan 16, 20233 years agoMIT

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Cite this paper

@article{cahyawijaya2023crosslingual,
  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},
  year   = {2023},
  eprint = {2306.14517},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.14517v1}
}

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