Now showing items 1-7 of 7

  • Acoustic feature prediction from semantic features for expressive speech using deep neural networks 

    Jauk, Igor; Bonafonte Cávez, Antonio; Pascual, Santiago (Institute of Electrical and Electronics Engineers (IEEE), 2016)
    Conference report
    Restricted access - publisher's policy
    The goal of the study is to predict acoustic features of expressive speech from semantic vector space representations. Though a lot of successful work was invested in expressiveness analysis and prediction, the results ...
  • Corpus for cyberbullying prevention 

    Moreno Bilbao, M. Asunción; Bonafonte Cávez, Antonio; Jauk, Igor; Tarrés, Laia; Pereira, Victor (International Speech Communication Association (ISCA), 2018)
    Conference report
    Open Access
    Cyberbullying is the use of digital media to harass a person or group of people, through personal attacks, disclosure of confidential or false information, among other means. That is to say, it ...
  • Creating expressive synthetic voices by unsupervised clustering of audiobooks 

    Jauk, Igor; Bonafonte Cávez, Antonio; López Otero, Paula; Docio Fernández, Laura (International Speech Communication Association (ISCA), 2015)
    Conference lecture
    Restricted access - publisher's policy
    In this work we design an approach for automatic feature selection and voice creation for expressive synthesis. Our approach is guided by two main goals: (1) increasing the flexibility of expressive voice creation and (2) ...
  • Direct expressive voice training based on semantic selection 

    Jauk, Igor; Bonafonte Cávez, Antonio (International Speech Communication Association (ISCA), 2016)
    Conference report
    Restricted access - publisher's policy
    This work aims at creating expressive voices from audiobooks using semantic selection. First, for each utterance of the audiobook an acoustic feature vector is extracted, including iVectors built on MFCC and on F0 ...
  • Expressive speech synthesis using sentiment embeddings 

    Jauk, Igor; Lorenzo Trueba, J.; Yamagishi, J.; Bonafonte Cávez, Antonio (International Speech Communication Association (ISCA), 2018)
    Conference report
    Open Access
    In this paper we present a DNN based speech synthesis system trained on an audiobook including sentiment features predicted by the Stanford sentiment parser. The baseline system uses DNN to predict acoustic parameters based ...
  • Prosodic and spectral iVectors for expressive speech synthesis 

    Jauk, Igor; Bonafonte Cávez, Antonio (Institute of Electrical and Electronics Engineers (IEEE), 2016)
    Conference lecture
    Open Access
    This work presents a study on the suitability of prosodic andacoustic features, with a special focus on i-vectors, in expressivespeech analysis and synthesis. For each utterance of two dif-ferent databases, a laboratory ...
  • Unsupervised learning for expressive speech synthesis 

    Jauk, Igor (Universitat Politècnica de Catalunya, 2017-09-12)
    Doctoral thesis
    Open Access
    Nowadays, especially with the upswing of neural networks, speech synthesis is almost totally data driven. The goal of this thesis is to provide methods for automatic and unsupervised learning from data for expressive speech ...