# French Listening Tests for the Assessment of Intelligibility, Quality, and Identity of Body-Conducted Speech Enhancement

arXiv: https://arxiv.org/abs/2506.04495 · HTML: https://mlsanity.com/paper/2506.04495/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 15 of 15)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [SoundStream: An End-to-End Neural Audio Codec](https://mlsanity.com/paper/2107.03312/) (2021)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [Moshi: a speech-text foundation model for real-time dialogue](https://mlsanity.com/paper/2410.00037/) (2024)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [Real Time Speech Enhancement in the Waveform Domain](https://mlsanity.com/paper/2006.12847/) (2020)
- [Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound](https://mlsanity.com/paper/2502.05139/) (2025)
- [SEANet: A Multi-modal Speech Enhancement Network](https://mlsanity.com/paper/2009.02095/) (2020)
- [URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement](https://mlsanity.com/paper/2406.04660/) (2024)
- [ECAPA2: A Hybrid Neural Network Architecture and Training Strategy for Robust Speaker Embeddings](https://mlsanity.com/paper/2401.08342/) (2024)
- [Speech Quality Assessment through MOS using Non-Matching References](https://mlsanity.com/paper/2206.12285/) (2022)
- [Vibravox: A Dataset of French Speech Captured with Body-conduction Audio Sensors](https://mlsanity.com/paper/2407.11828/) (2024)
- [EBEN: Extreme bandwidth extension network applied to speech signals captured with noise-resilient body-conduction microphones](https://mlsanity.com/paper/2210.14090/) (2022)
- [Enabling Real-time On-chip Audio Super Resolution for Bone Conduction Microphones](https://mlsanity.com/paper/2112.13156/) (2021)
- [Configurable EBEN: Extreme Bandwidth Extension Network to enhance body-conducted speech capture](https://mlsanity.com/paper/2303.10008/) (2023)

## Built on by (most cited first, 1 of 1)

- [Real-time speech enhancement in noise for throat microphone using neural audio codec as foundation model](https://mlsanity.com/paper/2508.02974/) (2025)

## Abstract (arXiv)

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.