# A Layer-Anchoring Strategy for Enhancing Cross-Lingual Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2407.04966 · HTML: https://mlsanity.com/paper/2407.04966/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 6 Jul 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [Exploring wav2vec 2.0 on speaker verification and language identification](https://mlsanity.com/paper/2012.06185/) (2020)
- [A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding](https://mlsanity.com/paper/2111.02735/) (2021)
- [ML-SUPERB: Multilingual Speech Universal PERformance Benchmark](https://mlsanity.com/paper/2305.10615/) (2023)
- [Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition](https://mlsanity.com/paper/2110.06309/) (2021)
- [Exploration of A Self-Supervised Speech Model: A Study on Emotional Corpora](https://mlsanity.com/paper/2210.02595/) (2022)
- [PEFT-SER: On the Use of Parameter Efficient Transfer Learning Approaches For Speech Emotion Recognition Using Pre-trained Speech Models](https://mlsanity.com/paper/2306.05350/) (2023)
- [An attention-based backend allowing efficient fine-tuning of transformer models for speaker verification](https://mlsanity.com/paper/2210.01273/) (2022)
- [Neural Representations for Modeling Variation in Speech](https://mlsanity.com/paper/2011.12649/) (2020)
- [A Study on Incorporating Whisper for Robust Speech Assessment](https://mlsanity.com/paper/2309.12766/) (2023)
- [Speaker and Language Change Detection using Wav2vec2 and Whisper](https://mlsanity.com/paper/2302.09381/) (2023)

## Built on by (most cited first, 6 of 6)

- [Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised Models](https://mlsanity.com/paper/2409.16920/) (2024)
- [Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2501.00995/) (2025)
- [Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment](https://mlsanity.com/paper/2407.17716/) (2024)
- [Mouth Articulation-Based Anchoring for Improved Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2412.19909/) (2024)
- [Speaker Style-Aware Phoneme Anchoring for Improved Cross-Lingual Speech Emotion Recognition](https://mlsanity.com/paper/2509.20373/) (2025)
- [Emotion as a Distribution: Joint Valence-Arousal Probability Learning for Speaker-Independent Multimodal Emotion Recognition](https://mlsanity.com/paper/2609.05755/) (2026)

## Abstract (arXiv)

Cross-lingual speech emotion recognition (SER) is important for a wide range of everyday applications. While recent SER research relies heavily on large pretrained models for emotion training, existing studies often concentrate solely on the final transformer layer of these models. However, given the task-specific nature and hierarchical architecture of these models, each transformer layer encapsulates different levels of information. Leveraging this hierarchical structure, our study focuses on the information embedded across different layers. Through an examination of layer feature similarity across different languages, we propose a novel strategy called a layer-anchoring mechanism to facilitate emotion transfer in cross-lingual SER tasks. Our approach is evaluated using two distinct language affective corpora (MSP-Podcast and BIIC-Podcast), achieving a best UAR performance of 60.21% on the BIIC-podcast corpus. The analysis uncovers interesting insights into the behavior of popular pretrained models.