评测EBEN模型在体传导语音增强中的表现，发现能提升音质和可懂度。
French Listening Tests for the Assessment of Intelligibility, Quality, and Identity of Body-Conducted Speech Enhancement
- 用EBEN模型增强体传导语音，结合听觉测试评估效果。
- 音质和可懂度显著提升，女性喉麦数据轻微影响辨识度。
- STOI与感知质量相关，适合语音增强与听觉评估研究者。
本研究通过听觉测试评估极端带宽扩展网络（EBEN）模型在体传导传感器上的表现。基于Vibravox数据集，采用法语改良押韵测试评估可懂度，使用MUSHRA协议评估语音质量，通过A/B识别任务评估说话人身份保留情况。实验涵盖额头加速度计、刚性耳塞麦克风和喉部麦克风采集的男女声信号。结果表明，EBEN显著提升语音质量和可懂度；但在女性喉部麦克风录音上轻微降低说话人识别性能。研究还发现短时客观可懂度（STOI）与感知质量存在相关性，且使用ECAPA2-TDNN的语音验证结果与识别表现一致。无现有指标可靠预测EBEN对可懂度的影响。
原文摘要 · Abstract (English)
This study evaluates the Extreme Bandwidth Extension Network (EBEN) model on body-conduction sensors through listening tests. Using the Vibravox dataset, we assess intelligibility with a French Modified Rhyme Test, speech quality with a MUSHRA (MUltiple Stimuli with Hidden Reference and Anchor) protocol and speaker identity preservation with an A/B identification task. The experiments involved male and female speakers recorded with a forehead accelerometer, rigid in-ear and throat microphones. The results confirm that EBEN enhances both speech quality and intelligibility. It slightly degrades speaker identification performance when applied to female speakers' throat microphone recordings. The findings also demonstrate a correlation between Short-Time Objective Intelligibility (STOI) and perceived quality in body-conducted speech, while speaker verification using ECAPA2-TDNN aligns well with identification performance. No tested metric reliably predicts EBEN's effect on intelligibility.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。