# MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and Hierarchical Cooperative Attention

arXiv: https://arxiv.org/abs/2404.13509 · HTML: https://mlsanity.com/paper/2404.13509/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 21 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [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)
- [Speech Emotion Recognition with Co-Attention based Multi-level Acoustic Information](https://mlsanity.com/paper/2203.15326/) (2022)
- [Emotion recognition by fusing time synchronous and time asynchronous representations](https://mlsanity.com/paper/2010.14102/) (2020)
- [Speech Sentiment Analysis via Pre-trained Features from End-to-end ASR Models](https://mlsanity.com/paper/1911.09762/) (2019)
- [Multimodal Emotion Recognition with High-level Speech and Text Features](https://mlsanity.com/paper/2111.10202/) (2021)
- [Speaker Normalization for Self-supervised Speech Emotion Recognition](https://mlsanity.com/paper/2202.01252/) (2022)
- [DST: Deformable Speech Transformer for Emotion Recognition](https://mlsanity.com/paper/2302.13729/) (2023)
- [Key-Sparse Transformer for Multimodal Speech Emotion Recognition](https://mlsanity.com/paper/2106.11532/) (2021)
- [DWFormer: Dynamic Window transFormer for Speech Emotion Recognition](https://mlsanity.com/paper/2303.01694/) (2023)

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

- [BSS-CFFMA: Cross-Domain Feature Fusion and Multi-Attention Speech Enhancement Network based on Self-Supervised Embedding](https://mlsanity.com/paper/2408.06851/) (2024)
- [VNet: A GAN-based Multi-Tier Discriminator Network for Speech Synthesis Vocoders](https://mlsanity.com/paper/2408.06906/) (2024)
- [Modality-Invariant Bidirectional Temporal Representation Distillation Network for Missing Multimodal Sentiment Analysis](https://mlsanity.com/paper/2501.05474/) (2025)
- [Heterogeneous Space Fusion and Dual-Dimension Attention: A New Paradigm for Speech Enhancement](https://mlsanity.com/paper/2408.06911/) (2024)
- [Qieemo: Speech Is All You Need in the Emotion Recognition in Conversations](https://mlsanity.com/paper/2503.22687/) (2025)
- [Speech Emotion Recognition Leveraging OpenAI's Whisper Representations and Attentive Pooling Methods](https://mlsanity.com/paper/2602.06000/) (2026)

## Abstract (arXiv)

Speech emotion recognition is crucial in human-computer interaction, but extracting and using emotional cues from audio poses challenges. This paper introduces MFHCA, a novel method for Speech Emotion Recognition using Multi-Spatial Fusion and Hierarchical Cooperative Attention on spectrograms and raw audio. We employ the Multi-Spatial Fusion module (MF) to efficiently identify emotion-related spectrogram regions and integrate Hubert features for higher-level acoustic information. Our approach also includes a Hierarchical Cooperative Attention module (HCA) to merge features from various auditory levels. We evaluate our method on the IEMOCAP dataset and achieve 2.6% and 1.87% improvements on the weighted accuracy and unweighted accuracy, respectively. Extensive experiments demonstrate the effectiveness of the proposed method.