Exploració per tema "i-vector"

Ara es mostren els items 1-4 de 4

    • Deep belief networks for i-vector based speaker recognition 

      Ghahabi Esfahani, Omid; Hernando Pericás, Francisco Javier (Institute of Electrical and Electronics Engineers (IEEE), 2014)
      Text en actes de congrés
      Accés restringit per política de l'editorial
      The use of Deep Belief Networks (DBNs) is proposed in this paper to model discriminatively target and impostor i-vectors in a speaker verification task. The authors propose to adapt the network parameters of each speaker ...

    • Restricted Boltzmann machines for vector representation of speech in speaker recognition 

      Ghahabi Esfahani, Omid; Hernando Pericás, Francisco Javier (Elsevier, 2018-01)
      Article
      Accés obert
      Over the last few years, i-vectors have been the state-of-the-art technique in speaker recognition. Recent advances in Deep Learning (DL) technology have improved the quality of i-vectors but the DL techniques in use are ...

    • Self-supervised deep learning approaches to speaker recognition: A Ph.D. Thesis overview 

      Khan, Umair; Hernando Pericás, Francisco Javier (International Speech Communication Association (ISCA), 2021)
      Comunicació de congrés
      Accés obert
      Recent advances in Deep Learning (DL) for speaker recognition have improved the performance but are constrained to the need of labels for the background data, which is difficult in prac- tice. In i-vector based speaker ...

    • Unsupervised training of siamese networks for speaker verification 

      Khan, Umair; Hernando Pericás, Francisco Javier (International Speech Communication Association (ISCA), 2020)
      Text en actes de congrés
      Accés obert
      Speaker labeled background data is an essential requirement for most state-of-the-art approaches in speaker recognition, e.g., xvectors and i-vector/PLDA. However, in reality it is difficult to access large amount of labeled ...