Voices Obscured in Complex Environmental Settings (VOICES) corpus
This provides a domain-specific resource for speech and signal processing researchers to improve models in realistic noisy environments, though it is incremental as it builds on existing datasets like LibriSpeech.
The paper introduces the VOICES corpus, a freely available dataset of 120 hours per microphone of far-field speech recorded in noisy room conditions to address the degradation of model performance in natural settings, aiming to advance distant microphone research.
This paper introduces the Voices Obscured In Complex Environmental Settings (VOICES) corpus, a freely available dataset under Creative Commons BY 4.0. This dataset will promote speech and signal processing research of speech recorded by far-field microphones in noisy room conditions. Publicly available speech corpora are mostly composed of isolated speech at close-range microphony. A typical approach to better represent realistic scenarios, is to convolve clean speech with noise and simulated room response for model training. Despite these efforts, model performance degrades when tested against uncurated speech in natural conditions. For this corpus, audio was recorded in furnished rooms with background noise played in conjunction with foreground speech selected from the LibriSpeech corpus. Multiple sessions were recorded in each room to accommodate for all foreground speech-background noise combinations. Audio was recorded using twelve microphones placed throughout the room, resulting in 120 hours of audio per microphone. This work is a multi-organizational effort led by SRI International and Lab41 with the intent to push forward state-of-the-art distant microphone approaches in signal processing and speech recognition.