多教师语言感知知识蒸馏用于多语言语音情感识别
Multi-Teacher Language-Aware Knowledge Distillation for Multilingual Speech Emotion Recognition
AI总结：
本文提出一种多教师语言感知知识蒸馏方法，用于提升英语、芬兰语和法语的语音情感识别性能，通过蒸馏多个教师模型的知识，实现多语言情感识别的高召回率。
摘要：
语音情感识别（SER）对于提高人机交互至关重要。尽管在单语SER领域已有进展，但将其扩展到构建多语言系统仍具挑战性。我们的目标是通过蒸馏多个教师模型的知识来训练一个能够处理多语言SER的单一模型。为此，我们提出了一种新颖的语言感知多教师知识蒸馏方法，以推进英语、芬兰语和法语的SER。该方法以Wav2Vec2.0作为单语教师模型的基础，然后将这些模型的知识蒸馏到一个单一的多语言学生模型中。学生模型在英语数据集上实现了72.9的加权召回率，在芬兰语数据集上实现了63.4的无加权召回率，超过了微调和知识蒸馏基线。我们的方法在提升悲伤和中性情绪的召回率方面表现出色，尽管在识别愤怒和快乐情绪时仍面临挑战。
英文摘要：
Speech Emotion Recognition (SER) is crucial for improving human-computer interaction. Despite strides in monolingual SER, extending them to build a multilingual system remains challenging. Our goal is to train a single model capable of multilingual SER by distilling knowledge from multiple teacher models. To address this, we introduce a novel language-aware multi-teacher knowledge distillation method to advance SER in English, Finnish, and French. It leverages Wav2Vec2.0 as the foundation of monolingual teacher models and then distills their knowledge into a single multilingual student model. The student model demonstrates state-of-the-art performance, with a weighted recall of 72.9 on the English dataset and an unweighted recall of 63.4 on the Finnish dataset, surpassing fine-tuning and knowledge distillation baselines. Our method excels in improving recall for sad and neutral emotions, although it still faces challenges in recognizing anger and happiness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
