EmotionAI：一种面向语音情感驱动的对话分析的隐私保护计算智能流水线
EmotionAI: A Privacy-Preserving Computational Intelligence Pipeline for Speech-Emotion-Grounded Conversational Analysis
AI总结：
提出一种完全本地的计算智能流水线，结合语音情感识别与生成式推理，实现隐私保护的对话情感分析，在RAVDESS数据集上达到48.8%准确率，零外部调用。
摘要：
审查录制的访谈以获取情感线索（如镇定、犹豫和激动）既缓慢又主观，而能够自动化的云服务需要将敏感音频传出设备。EmotionAI 是一个完全本地的计算智能（CI）流水线，它将语音情感识别（SER）与生成式推理相结合。说话人日志、Whisper 自动语音识别（ASR）和 wav2vec2 情感分类器生成每段的情感证据，然后将其传递给一个对抗性的三模型本地大语言模型（LLM）面板，用于基于时间戳和引用约束的问答。在 RAVDESS 四类英语子集（n=672）上的零样本评估揭示了跨语料库的脆弱性而非分类器的优越性：部署的分类器准确率为 48.8%，高于随机基线（24.9%）和多数基线（28.6%），但低于领域内 MFCC + 逻辑回归比较器（71.0%）。整个流水线在 CPU 上平均运行 157 秒（实时因子约 1.33），零外部调用。贡献不在于最先进的 SER，而在于将不完美的情感证据可审计、隐私保护地整合到基于对话的分析中，并诚实地报告了跨语料库迁移和以人为中心的验证仍存在的不足。
英文摘要：
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.
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。
