Research Output
Acoustic Model Adaptation from Raw Waveforms with Sincnet
  Raw waveform acoustic modelling has recently gained interest due to neural networks' ability to learn feature extraction, and the potential for finding better representations for a given scenario than hand-crafted features. SincNet has been proposed to reduce the number of parameters required in raw-waveform modelling, by restricting the filter functions, rather than having to learn every tap of each filter. We study the adaptation of the SincNet filter parameters from adults' to children's speech, and show that the parameterisation of the SincNet layer is well suited for adaptation in practice: we can efficiently adapt with a very small number of parameters, producing error rates comparable to techniques using orders of magnitude more parameters.

  • Date:

    31 December 2019

  • Publication Status:


  • Publisher


  • DOI:


  • Funders:

    Engineering and Physical Sciences Research Council


Fainberg, J., Klejch, O., Loweimi, E., Bell, P., & Renals, S. (2019). Acoustic Model Adaptation from Raw Waveforms with Sincnet. In 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).


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