Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition
This addresses the limited applicability of speech emotion recognition for low-resource elderly populations in non-English languages, though it is incremental as it builds on existing methods with new datasets.
The study tackled the problem of speech emotion recognition being biased toward English-speaking adults by analyzing transferability across languages (English, Mandarin, Cantonese) and age groups (adults, elderly), finding that cross-lingual inference is unsuitable due to specific feature requirements, but cross-group data augmentation helps regularize models with linguistic distance affecting transferability.
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.