mlsanityData to 7 Oct 2026

Vibravox: A Dataset of French Speech Captured with Body-conduction Audio Sensors

arXiv 2407.11828 ↗First posted 16 Jul 2024eess.AS cs.LGcited by 5

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Abstract (arXiv)

Vibravox is a dataset compliant with the General Data Protection Regulation (GDPR) containing audio recordings using five different body-conduction audio sensors: two in-ear microphones, two bone conduction vibration pickups, and a laryngophone. The dataset also includes audio data from an airborne microphone used as a reference. The Vibravox corpus contains 45 hours per sensor of speech samples and physiological sounds recorded by 188 participants under different acoustic conditions imposed by a high order ambisonics 3D spatializer. Annotations about the recording conditions and linguistic transcriptions are also included in the corpus. We conducted a series of experiments on various speech-related tasks, including speech recognition, speech enhancement, and speaker verification. These experiments were carried out using state-of-the-art models to evaluate and compare their performances on signals captured by the different audio sensors offered by the Vibravox dataset, with the aim of gaining a better grasp of their individual characteristics.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 40 of its 103 references that are in the map. First posted on arXiv 16 Jul 2024.

Builds on

40 of 103 references.

References · 40

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28 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMscited by 4
  2. 2025Analyzing the relationships between pretraining language, phonetic, tonal, and speaker information in self-supervised speech modelscited by 3
  3. 2025Throat and acoustic paired speech dataset for deep learning-based speech enhancementcited by 1
  4. 2025SUBARU: A Practical Approach to Power Saving in Hearables Using SUB-Nyquist Audio Resolution Upsamplingcited by 0
  5. 2026BCoughBench: Benchmarking Respiratory Acoustic Foundation Models Under Body-Coupled Wearable Sensor Conditionscited by 0