本地化情感分析系统，保护隐私的同时实现对话情绪溯源。
EmotionAI: A Privacy-Preserving Computational Intelligence Pipeline for Speech-Emotion-Grounded Conversational Analysis
- 语音情绪识别与生成推理结合，全程本地运行
- 跨语料测试准确率48.8%，低于理想模型但高于基线
- 适合需隐私保护的访谈分析场景，如心理评估
对录音访谈中情绪线索（如镇定、激动）的审阅耗时且主观，而云端自动化服务需上传敏感音频。EmotionAI 是一个完全本地的计算智能管道，将语音情绪识别（SER）与生成推理结合。通过说话人分离、Whisper自动语音识别（ASR）和 wav2vec2 情绪分类器生成每段音频的情绪证据，并由对抗性三模型本地大语言模型（LLM）面板将其转化为带时间戳、引用约束的答案。在 RAVDESS 四类英语子集（n = 672）上进行零样本评估，显示跨语料迁移代价：部署模型准确率为48.8%，高于随机（24.9%）和多数类基线（28.6%），但低于域内 MFCC + 逻辑回归比较器（71.0%）。整个管道在 CPU 上平均耗时157秒（实时因子约1.33），无外部调用。贡献不在于最先进的语音情绪识别，而在于可审计、隐私保护地将不完美的情绪证据整合进有依据的对话分析。
原文摘要 · Abstract (English)
Reviewing recorded interviews for affective cues such as composure and agitation is slow and subjective, and cloud services that could automate the task require sensitive audio to leave the device. EmotionAI is a fully local Computational Intelligence (CI) pipeline that couples Speech Emotion Recognition (SER) with generative reasoning. Speaker diarisation, Whisper Automatic Speech Recognition (ASR) and a wav2vec2 emotion classifier produce per-segment affective evidence, and an adversarial three-model local Large Language Model (LLM) panel turns that evidence into timestamp-grounded, citation-constrained answers. Zero-shot evaluation on the RAVDESS four-class English subset (n = 672) measures the cost of cross-corpus transfer: the deployed classifier scores 48.8% accuracy, above random (24.9%) and majority (28.6%) baselines but below an in-domain MFCC + logistic-regression comparator (71.0%). The complete pipeline runs in a mean 157 s on CPU (real-time factor approximately 1.33) with zero external calls. The contribution is not state-of-the-art SER but an auditable, privacy-preserving integration of imperfect affective evidence into grounded conversational analysis.
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