Vibravox: A Dataset of French Speech Captured with Body-conduction Audio Sensors
This work provides a GDPR-compliant dataset for researchers and engineers working on speech technology, enabling better understanding of body-conduction sensors, but it is incremental as it focuses on data collection and benchmarking rather than novel methods.
The researchers tackled the problem of evaluating body-conduction audio sensors for speech tasks by creating the Vibravox dataset, which includes 45 hours per sensor of speech and physiological sounds from 188 participants under varied acoustic conditions, and they conducted experiments on tasks like speech recognition and enhancement using state-of-the-art models to compare sensor performances.
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.