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Voices Obscured in Complex Environmental Settings (VOICES) corpus

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arxiv 1804.05053 v2 pith:M7F7CHQW submitted 2018-04-13 cs.SD eess.AS

classification cs.SDeess.AS
keywords speechcorpusrecordedroomvoicesaudionoiseavailable
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. How Would It Sound? Material-Controlled Multimodal Acoustic Profile Generation for Indoor Scenes

    cs.CV 2025-08 unverdicted novelty 6.0 of 10

    A user-changeable material mask lets an encoder-decoder generate a room's impulse response from a single audio-visual observation, trained and evaluated on the new Acoustic Wonderland Dataset.

  2. Beyond Words: Interjection Classification for Improved Human-Computer Interaction

    cs.HC 2025-09 conditional novelty 5.0 of 10

    Interjection classification over five speakers improves when pitch, tempo, and background-noise augmentation is added, but absolute accuracy stays below 60% on unseen speakers.

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