Feature Learning with Raw-Waveform CLDNNs for Voice Activity Detection
By Rubén Zazo-Candil, Tara N. Sainath, Gabor Simko, Carolina Parada. 2016
126 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Representation & Self-Supervised Learning, Speech & Audio, Signal Processing
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Citations in the corpus
- BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition (2021, methodology): “For each language we prepare an unlabeled YouTube dataset segmented using voice activation detection (VAD [82]), the labeled Voice Search dataset and its 100 h and 1000 h subsets.”
- Speaker Diarization with LSTM (2017, methodology): “r. It only has two full covariance Gaussians: one for speech, and one for non-speech. We found this simple VAD generalizes better across domains (from queries to telephone) for diarization than CLDNN [24] VAD models. 4.2. Datasets We report Diarization Error Rates (DER) on three standard public data”
- Self-supervised Learning with Random-projection Quantizer for Speech Recognition (2022, methodology): “For each language we prepare an unlabeled YouTube dataset segmented using voice activation detection (VAD (Zazo Candil et al., 2016)).”