399 citations · 591 across the 32 of their papers we have counts for
6 papers · 1 filter
Self-supervised pre-training with acoustic configurations for replay spoofing detection
Hye-jin Shim, Hee-Soo Heo, Jee-weon Jung +1
Constructing a dataset for replay spoofing detection requires a physical process of playing an utterance and re-recording it, presenting a challenge to the collection of large-scal…
Cosine similarity-based adversarial process
Hee-Soo Heo, Jee-weon Jung, Hye-jin Shim +2
An adversarial process between two deep neural networks is a promising approach to train a robust model. In this paper, we propose an adversarial process using cosine similarity, w…
Acoustic scene classification using teacher-student learning with soft-labels
Hee-Soo Heo, Jee-weon Jung, Hye-jin Shim +1
Acoustic scene classification identifies an input segment into one of the pre-defined classes using spectral information. The spectral information of acoustic scenes may not be mut…
Replay attack detection with complementary high-resolution information using end-to-end DNN for the ASVspoof 2019 Challenge
Jee-weon Jung, Hye-jin Shim, Hee-Soo Heo +1
In this study, we concentrate on replacing the process of extracting hand-crafted acoustic feature with end-to-end DNN using complementary high-resolution spectrograms. As a result…
RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification
Jee-weon Jung, Hee-Soo Heo, Ju-ho Kim +2
Recently, direct modeling of raw waveforms using deep neural networks has been widely studied for a number of tasks in audio domains. In speaker verification, however, utilization…
End-to-end losses based on speaker basis vectors and all-speaker hard negative mining for speaker verification
Hee-Soo Heo, Jee-weon Jung, IL-Ho Yang +3
In recent years, speaker verification has primarily performed using deep neural networks that are trained to output embeddings from input features such as spectrograms or Mel-filte…