Explainable Lightweight Compact Deep Models for Speech Emotion Recognition

• URL: http://arxiv.org/abs/2607.16803v1
• Date: Sat, 18 Jul 2026 12:50:33 GMT
• Title: Explainable Lightweight Compact Deep Models for Speech Emotion Recognition
• Abstract summary: Speech Emotion Recognition (SER) is an important component in a wide range of human-centered applications.<n>This paper presents an explainable and lightweight speech emotion recognition framework based on a compact convolutional neural network architecture.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech Emotion Recognition (SER) is an important component in a wide range of human-centered applications, including healthcare, customer service, and human-omputer interaction. In medical and decision-support settings, there is increasing interest in models that not only achieve accurate emotion recognition but also support transparent predictions and efficient deployment. However, many existing SER approaches rely on complex deep learning architectures that limit interpretability and increase computational cost. This paper presents an explainable and lightweight speech emotion recognition framework based on a compact convolutional neural network architecture. The proposed approach utilizes log-Mel spectrogram representations to capture spectro-temporal speech characteristics and employs attentive statistics pooling to emphasize emotionally salient temporal segments. To improve model transparency, gradient-based class activation mapping (Grad-CAM) is incorporated to visualize the time-frequency regions that influence the model's predictions. Experimental evaluation on the SAVEE emotional speech dataset demonstrates that the proposed framework achieves competitive recognition performance while maintaining a compact architecture with significantly fewer parameters than many existing SER models. The results indicate that efficient convolutional architectures combined with interpretable analysis can provide a practical balance between recognition accuracy, computational efficiency, and model transparency.

Related papers

• Enhancing Multimodal Emotion Recognition via Multi-Feature Encoding and Attention-Based Fusion [5.740228163421595]
  This paper proposes a novel multimodal emotion recognition framework.<n>It integrates rich audio and visual feature extraction with an attention-based fusion strategy.<n>Our findings suggest that the proposed framework effectively captures diverse emotional cues from speech and visual expressions.
  arXiv  Detail & Related papers  (2026-09-04T03:40:16Z)
• ViQ: Text-Aligned Visual Quantized Representations at Any Resolution [91.46185855575789]
  We present ViQ, a Visual Quantized Representations framework, which is designed to balance semantics and details in discrete representations.<n>Our approach structures quantization learning into two stages: text-aligned pre-training and feature discretization.<n>Experiments on multimodal tasks demonstrate that ViQ achieves competitive performance compared to state-of-the-art multimodal vision encoders.
  arXiv  Detail & Related papers  (2026-06-25T17:29:27Z)
• A Lightweight Transformer for Pain Recognition from Brain Activity [11.72500739025842]
  This paper presents a lightweight transformer architecture that fuses multiple fNIRS representations through a unified tokenization mechanism.<n>The proposed token-mixing strategy preserves spatial, temporal, and time-frequency characteristics by projecting heterogeneous inputs onto a shared latent representation.<n>The model is evaluated on the AI4Pain dataset using stacked raw waveform and power spectral density representations of fNIRS inputs.
  arXiv  Detail & Related papers  (2026-04-13T13:25:19Z)
• Multimodal Emotion Recognition via Bi-directional Cross-Attention and Temporal Modeling [2.8037951156321377]
  We propose a multimodal emotion recognition framework for the Expression task in the 10th Affective Behavior Analysis in-the-wild (ABAW) Challenge.<n>Our framework builds on large-scale pre-trained models for visual and audio representation learning and integrates them in a unified multimodal architecture.<n> Experimental results on the ABAW 10th EXPR benchmark demonstrate the effectiveness of the proposed framework.
  arXiv  Detail & Related papers  (2026-03-12T14:20:29Z)
• LeMoRe: Learn More Details for Lightweight Semantic Segmentation [48.81126061219231]
  We introduce an efficient paradigm by synergizing explicit and implicit modeling to balance computational efficiency with representational fidelity.<n>Our method combines well-defined Cartesian directions with explicitly modeled views and implicitly inferred intermediate representations, efficiently capturing global dependencies.
  arXiv  Detail & Related papers  (2025-05-29T04:55:10Z)
• Efficient Speech Language Modeling via Energy Distance in Continuous Latent Space [74.12387631212609]
  We introduce SLED, an alternative approach to speech language modeling by encoding speech waveforms into sequences of continuous latent representations.<n>SLED avoids discretization errors and eliminates the need for the complicated hierarchical architectures common in existing speech language models.<n> Empirical results demonstrate that SLED achieves strong performance in both zero-shot and streaming speech synthesis.
  arXiv  Detail & Related papers  (2025-05-19T14:38:59Z)
• Rethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecture [2.3272964989267626]
  We propose a lightweight, yet effective fusion-based deep learning model tailored for utterance-level emotion classification.<n>Our approach demonstrates that with careful feature engineering and modular design, simpler fusion strategies can outperform or match more complex models.
  arXiv  Detail & Related papers  (2025-05-05T02:31:11Z)
• Unveiling Hidden Factors: Explainable AI for Feature Boosting in Speech Emotion Recognition [17.568724398229232]
  Speech emotion recognition (SER) has gained significant attention due to its several application fields, such as mental health, education, and human-computer interaction. This study proposes an iterative feature boosting approach for SER that emphasizes feature relevance and explainability to enhance machine learning model performance. The effectiveness of the proposed method is validated on the SER benchmarks of the Toronto emotional speech set (TESS), Berlin Database of Emotional Speech (EMO-DB), Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), and Surrey Audio-Visual Expressed Emotion (SAVEE) datasets.
  arXiv  Detail & Related papers  (2024-06-01T00:39:55Z)
• Iterative Feature Boosting for Explainable Speech Emotion Recognition [17.568724398229232]
  We present a new supervised SER method based on an efficient feature engineering approach. We pay particular attention to the explainability of results to evaluate feature relevance and refine feature sets. The proposed method outperforms human-level performance (HLP) and state-of-the-art machine learning methods in emotion recognition on the TESS dataset.
  arXiv  Detail & Related papers  (2024-05-30T15:44:27Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation [56.264157127549446]
  Speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction. One of the main challenges in SER is data scarcity. We propose a transfer learning strategy combined with spectrogram augmentation.
  arXiv  Detail & Related papers  (2021-08-05T10:39:39Z)
• A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
  We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn) Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-04T14:59:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.