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

arXiv: https://arxiv.org/abs/2306.14517 · HTML: https://mlsanity.com/paper/2306.14517/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 52 references that are in the map.
- First posted on arXiv 26 Jun 2023.

## Builds on (most cited first, 26 of 26)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [Exploring wav2vec 2.0 on speaker verification and language identification](https://mlsanity.com/paper/2012.06185/) (2020)
- [Multi-attention Recurrent Network for Human Communication Comprehension](https://mlsanity.com/paper/1802.00923/) (2018)
- [NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages](https://mlsanity.com/paper/2205.15960/) (2022)
- [Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank Transformer](https://mlsanity.com/paper/1910.13923/) (2019)
- [ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation](https://mlsanity.com/paper/2112.06223/) (2021)
- [Multimodal End-to-End Sparse Model for Emotion Recognition](https://mlsanity.com/paper/2103.09666/) (2021)
- [NusaCrowd: Open Source Initiative for Indonesian NLP Resources](https://mlsanity.com/paper/2212.09648/) (2022)
- [XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering](https://mlsanity.com/paper/1905.11471/) (2019)
- [Learning Fast Adaptation on Cross-Accented Speech Recognition](https://mlsanity.com/paper/2003.01901/) (2020)
- [CI-AVSR: A Cantonese Audio-Visual Speech Dataset for In-car Command Recognition](https://mlsanity.com/paper/2201.03804/) (2022)
- [ERICA: An Empathetic Android Companion for Covid-19 Quarantine](https://mlsanity.com/paper/2106.02325/) (2021)
- [Greenformer: Factorization Toolkit for Efficient Deep Neural Networks](https://mlsanity.com/paper/2109.06762/) (2021)
- [Greenformers: Improving Computation and Memory Efficiency in Transformer Models via Low-Rank Approximation](https://mlsanity.com/paper/2108.10808/) (2021)
- [Nora: The Well-Being Coach](https://mlsanity.com/paper/2106.00410/) (2021)
- [Weakly-supervised Multi-task Learning for Multimodal Affect Recognition](https://mlsanity.com/paper/2104.11560/) (2021)
- [SeMemNN: A Semantic Matrix-Based Memory Neural Network for Text Classification](https://mlsanity.com/paper/2003.01857/) (2020)
- [Speech Artifact Removal from EEG Recordings of Spoken Word Production with Tensor Decomposition](https://mlsanity.com/paper/2206.00635/) (2022)
- [What Did I Just Hear? Detecting Pornographic Sounds in Adult Videos Using Neural Networks](https://mlsanity.com/paper/2209.03711/) (2022)
- [AI-Based Emotion Recognition: Promise, Peril, and Prescriptions for Prosocial Path](https://mlsanity.com/paper/2211.07290/) (2022)

## Built on by (most cited first, 1 of 1)

- [Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning](https://mlsanity.com/paper/2309.15317/) (2023)

## Abstract (arXiv)

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.