Specifically, the study focuses on generating high-quality neural speaker representations without any annotated data, as well as on estimating secondary hyperparameters of the model without annotations.
We decompose speech into discrete and disentangled learned representations, consisting of content units, F0, speaker, and emotion.
We use a decomposition of the speech signal into discrete learned representations, consisting of phonetic-content units, prosodic features, speaker, and emotion.
Results suggest that our approach surpasses the baseline models and reaches state-of-the-art performance on both data sets.
Phoneme boundary detection plays an essential first step for a variety of speech processing applications such as speaker diarization, speech science, keyword spotting, etc.
Steganography is the science of hiding a secret message within an ordinary public message, which is referred to as Carrier.
Deep learning models have been successfully applied to malware detection.
We also present two black-box attacks: where the adversarial examples were generated with a system that was trained on YOHO, but the attack is on a system that was trained on NTIMIT; and when the adversarial examples were generated with a system that was trained on Mel-spectrum feature set, but the attack is on a system that was trained on MFCC.