Speech Emotion Recognition (SER) needs high computational resources to overcome the challenge of substantial annotator disagreement. Today SER is shifting towards dimensional annotations of arousal, dominance, and valence (A/D/V). Universal metrics as the L2 distance prove unsuitable for evaluating A/D/V accuracy due to non converging consensus of annotator opinions. However, Concordance Correlation Coefficient (CCC) arose as an alternative metric for A/D/V where a model's output is evaluated to match a whole dataset's CCC rather than L2 distances of individual audios. Recent studies have shown that Wav2Vec2.0 / WavLM architectures outputing a float value for each A/D/V dimension achieve today's State-of-the-art (SOTA) CCC on A/D/V. The Wav2Vec2.0 / WavLM family has high computational footprint, but training tiny models using human annotations has been unsuccessful. In this paper we use a large Transformer SOTA A/D/V model as Teacher/Annotator to train 5 student models: 4 MobileNets and our proposed Wav2Small, using only the Teacher's A/D/V predictions instead of human annotations. We chose MobileNet-V4 / MobileNet-V3 as students, as MobileNet has been designed for fast execution times. We propose Wav2Small an architecture designed for minimal parameter number and RAM consumption. Wav2Small with an .onnx (quantized) of only $60KB$ is a potential solution for A/D/V on hearing aids, having only 72K parameters vs 3.12M parameters for MobileNet-V4-Small. The Teacher model we construct sets a new SOTA on the MSP Podcast Test-1 dataset with valence CCC=0.676.

翻译：语音情感识别需要大量计算资源以克服标注者间存在显著分歧的挑战。当前语音情感识别正转向对唤醒度、支配度和效价的三维标注。由于标注者意见无法达成一致共识，L2距离等通用度量标准被证明不适用于评估A/D/V的准确性。然而，一致性相关系数作为A/D/V的替代度量标准应运而生，该标准通过评估模型输出与整个数据集CCC的匹配程度，而非单个音频的L2距离。近期研究表明，为每个A/D/V维度输出浮点值的Wav2Vec2.0/WavLM架构在当前A/D/V的CCC指标上达到最先进水平。Wav2Vec2.0/WavLM系列模型计算开销较大，而使用人工标注训练微型模型尚未成功。本文采用大型Transformer架构的SOTA A/D/V模型作为教师/标注器，仅使用该模型的A/D/V预测值（而非人工标注）训练了5个学生模型：包括4个MobileNet模型及我们提出的Wav2Small。选择MobileNet-V4/MobileNet-V3作为学生模型，因其专为快速执行而设计。我们提出Wav2Small架构，该架构以实现最小参数量和内存消耗为目标。Wav2Small的量化.onnx模型仅60KB，参数量仅72K（MobileNet-V4-Small为312万），有望成为助听器设备实现A/D/V识别的潜在解决方案。我们构建的教师模型在MSP Podcast Test-1数据集上创造了效价CCC=0.676的新SOTA记录。