用预训练模型+高效调参，让语音情感识别在普通电脑上快速达到高精度。
Enhancing Speech Emotion Recognition via Fine-Tuning Pre-Trained Models and Hyper-Parameter Optimisation
- 用wav2vec2模型微调，结合贝叶斯优化自动调参。
- 最优结果达0.97准确率，比网格搜索快100倍以上。
- 适合资源有限但追求高精度的语音识别研究者。
我们提出一种语音情感识别（SER）工作流，结合预训练表示与自动化超参数优化（HPO）。采用SpeechBrain的wav2vec2-base模型在IEMOCAP上微调作为编码器，在相同的四维搜索空间和15次试验预算下，对比高斯过程贝叶斯优化（GP-BO）与树结构帕累托估计器（TPE）策略，以德国EmoDB语料库上的平衡类别准确率（BCA）为目标。所有实验在8核CPU、32GB内存环境下运行。GP-BO在11分钟内达成0.96 BCA，TPE（Hyperopt实现）在15分钟内达到0.97。相比之下，网格搜索需143次试验、1680分钟才能超过0.9 BCA，而最佳的AutoSpeech 2020基线仅在GPU上30分钟达到0.85。跨语言泛化方面，基于EmoDB训练并经HPO调优的模型，在CREMA-D和RAVDESS上实现零样本准确率提升0.25和0.26。结果表明，结合预训练编码器与高效HPO可在通用CPU上实现竞争力强的SER性能。源代码见：https://github.com/youngaryan/speechbrain-emotion-hpo。
原文摘要 · Abstract (English)
We propose a workflow for speech emotion recognition (SER) that combines pre-trained representations with automated hyperparameter optimisation (HPO). Using SpeechBrain wav2vec2-base model fine-tuned on IEMOCAP as the encoder, we compare two HPO strategies, Gaussian Process Bayesian Optimisation (GP-BO) and Tree-structured Parzen Estimators (TPE), under an identical four-dimensional search space and 15-trial budget, with balanced class accuracy (BCA) on the German EmoDB corpus as the objective. All experiments run on 8 CPU cores with 32 GB RAM. GP-BO achieves 0.96 BCA in 11 minutes, and TPE (Hyperopt implementation) attains 0.97 in 15 minutes. In contrast, grid search requires 143 trials and 1,680 minutes to exceed 0.9 BCA, and the best AutoSpeech 2020 baseline reports only 0.85 in 30 minutes on GPU. For cross-lingual generalisation, an EmoDB-trained HPO-tuned model improves zero-shot accuracy by 0.25 on CREMA-D and 0.26 on RAVDESS. Results show that efficient HPO with pre-trained encoders delivers competitive SER on commodity CPUs. Source code to this work is available at: https://github.com/youngaryan/speechbrain-emotion-hpo.
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