Browsing NTNU Open by Author "Sabzi Shahrebabaki, Abdolreza"

Now showing items 1-8 of 8

    • A Comparative Study of Deep Learning Techniques on Frame-Level Speech Data Classification 

      Sabzi Shahrebabaki, Abdolreza; Imran, Ali Shariq; Olfati, Negar; Svendsen, Torbjørn Karl (Journal article; Peer reviewed, 2019)
      This paper provides a comprehensive analysis of the effect of speaking rate on frame classification accuracy. Different speaking rates may affect the performance of the automatic speech recognition system yielding poor ...

    • Acoustic Feature Comparison for Different Speaking Rates 

      Sabzi Shahrebabaki, Abdolreza; Imran, Ali Shariq; Olfati, Negar; Svendsen, Torbjørn Karl (Chapter, 2018)
      This paper investigates the effect of speaking rate variation on the task of frame classification. This task is indicative of the performance on phoneme and word recognition and is a first step towards designing voice-controlled ...

    • Acoustic-to-Articulatory Mapping With Joint Optimization of Deep Speech Enhancement and Articulatory Inversion Models 

      Sabzi Shahrebabaki, Abdolreza; Salvi, Giampiero; Svendsen, Torbjørn Karl; Siniscalchi, Sabato Marco (Journal article; Peer reviewed, 2021)
      We investigate the problem of speaker independent acoustic-to-articulatory inversion (AAI) in noisy conditions within the deep neural network (DNN) framework. In contrast with recent results in the literature, we argue ...

    • A DNN Based Speech Enhancement Approach to Noise Robust Acoustic-to-Articulatory Inversion 

      Sabzi Shahrebabaki, Abdolreza; Siniscalchi, Sabato Marco; Salvi, Giampiero; Svendsen, Torbjørn Karl (Chapter, 2021)
      In this work, we investigate the problem of speaker independent acoustic-to-articulatory inversion (AAI) in noisy condition within the deep neural network (DNN) framework. We claim that DNN vector-to-vector regression for ...

    • Noise Robustness in Small-Vocabulary Speech Recognition 

      Haflan, Vetle (Master thesis, 2019)
      Denne masteroppgaven omhandler små-vokabular talegjenkjenning, og mer spesifikt støyrobusthet i systemer designet for dette formål. Tradisjonelle og moderne gjenkjenningssystemer har blitt trent på relativt store mengder ...

    • Sequence-to-sequence articulatory inversion through time convolution of sub-band frequency signals 

      Sabzi Shahrebabaki, Abdolreza; Siniscalchi, Sabato Marco; Salvi, Giampiero; Svendsen, Torbjørn Karl (Peer reviewed; Journal article, 2020)
      We propose a new acoustic-to-articulatory inversion (AAI) sequence-to-sequence neural architecture, where spectral sub-bands are independently processed in time by 1-dimensional (1-D) convolutional filters of different ...

    • Transfer learning of articulatory information through phone information. 

      Sabzi Shahrebabaki, Abdolreza; Olfati, Negar; Siniscalchi, Sabato Marco; Salvi, Giampiero; Svendsen, Torbjørn Karl (Journal article; Peer reviewed, 2020)
      Articulatory information has been argued to be useful for several speech tasks. However, in most practical scenarios this information is not readily available. We propose a novel transfer learning framework to obtain ...

    • A Two-Stage Deep Modeling Approach to Articulatory Inversion 

      Sabzi Shahrebabaki, Abdolreza; Olfati, Negar; Imran, Ali Shariq; Johnsen, Magne Hallstein; Siniscalchi, Sabato Marco; Svendsen, Torbjørn Karl (Chapter, 2021)
      This paper proposes a two-stage deep feed-forward neural network (DNN) to tackle the acoustic-to-articulatory inversion (AAI) problem. DNNs are a viable solution for the AAI task, but the temporal continuity of the estimated ...