Voices Obscured in Complex Environmental Settings (VOICES) corpus
arXiv:1804.05053
Abstract
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.
Submitted to Interspeech 2018
Cited by in corpus (14)
- Interpretable Representation Learning for Speech and Audio Signals Based on Relevance Weighting
- Speaker Recognition Based on Deep Learning: An Overview
- Improving Noise Robustness of an End-to-End Neural Model for Automatic Speech Recognition
- A Pyramid Recurrent Network for Predicting Crowdsourced Speech-Quality Ratings of Real-World Signals
- The INTERSPEECH 2020 Far-Field Speaker Verification Challenge
- Structural sparsification for Far-field Speaker Recognition with GNA
- DEAAN: Disentangled Embedding and Adversarial Adaptation Network for Robust Speaker Representation Learning
- Diarization of Legal Proceedings. Identifying and Transcribing Judicial Speech from Recorded Court Audio
- Reducing audio membership inference attack accuracy to chance: 4 defenses
- Improving Reverberant Speech Separation with Multi-stage Training and Curriculum Learning
- Designing Neural Speaker Embeddings with Meta Learning
- Empowering cyberphysical systems of systems with intelligence
- A comparison of streaming models and data augmentation methods for robust speech recognition
- Parameterized Channel Normalization for Far-field Deep Speaker Verification