Speech Emotion Recognition using Attention-based LSTM-Network with Residual Connection
Speech emotion recognition is an important component of modern human-computer interaction systems. However, many state-of-the-art approaches rely on large pretrained models with high computational and memory requirements, limiting their applicability. This paper proposes ResLSTM-SA, a lightweight architecture that integrates residual connections with soft attention within an LSTM-based framework. Evaluated on the RAVDESS dataset under strict speaker-independent partitioning, the proposed model outperforms conventional attention-based LSTM baselines and several previously reported CNN- and hybrid CNN-LSTM architectures in terms of unweighted average recall (UAR). The best-performing varian...
| Authors | Daniil Krasnoproshin, Maxim Vashkevich |
|---|---|
| Primary category | cs.SD |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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