动态参数记忆：用于对话中长序列情感识别的临时LoRA增强大语言模型
Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation
AI总结：
本文提出动态参数记忆机制，通过临时LoRA模块编码句子级情感，使语音大模型在有限上下文下处理无限长音频，在IEMOCAP上取得最优情感识别性能。
摘要：
近期研究聚焦于应用语音大语言模型（SLLM）来改进语音情感识别（SER）。然而，语音模态固有的高帧率严重限制了SLLM的信号处理和理解能力。例如，一个具有4K上下文窗口的SLLM在50Hz特征采样率下，仅能处理80秒的音频便达到其容量极限。SLLM中使用的输入令牌压缩方法忽视了多轮对话中情感的连续性和惯性。本文提出了一种带有上下文语义和句子级情感编码的动态参数记忆（DPM）机制，使SLLM能够在有限上下文窗口下处理无限长度的音频。具体而言，DPM在推理过程中逐步将句子级信息和情感编码到临时LoRA模块中，以有效“记忆”上下文信息。我们训练了一个情感SLLM作为骨干模型，并将DPM集成到推理中用于对话中的情感识别（ERC）。在IEMOCAP数据集上的实验结果表明，DPM在处理长音频序列时显著提升了SLLM的情感识别能力，达到了最先进的性能。
英文摘要：
Recent research has focused on applying speech large language model (SLLM) to improve speech emotion recognition (SER). However, the inherently high frame rate in speech modality severely limits the signal processing and understanding capabilities of SLLM. For example, a SLLM with a 4K context window can only process 80 seconds of audio at 50Hz feature sampling rate before reaching its capacity limit. Input token compression methods used in SLLM overlook the continuity and inertia of emotions across multiple conversation turns. This paper proposes a Dynamic Parameter Memory (DPM) mechanism with contextual semantics and sentence-level emotion encoding, enabling processing of unlimited-length audio with limited context windows in SLLM. Specifically, DPM progressively encodes sentence-level information and emotions into a temporary LoRA module during inference to effectively "memorize" the contextual information. We trained an emotion SLLM as a backbone and incorporated our DPM into inference for emotion recognition in conversation (ERC). Experimental results on the IEMOCAP dataset show that DPM significantly improves the emotion recognition capabilities of SLLM when processing long audio sequences, achieving state-of-the-art performance.
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